Automated fault detection and diagnostics in a building management system

ABSTRACT

Systems and methods for building automation system management are shown and described. The systems and methods relate to fault detection via abnormal energy monitoring and detection. The systems and methods also relate to control and fault detection methods for chillers. The systems and methods further relate to graphical user interfaces for use with fault detection features of a building automation system.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.13/246,644, filed Sep. 27, 2011; which is a continuation in part of U.S.application Ser. No. 12/949,660, filed Nov. 18, 2010; which is acontinuation in part of U.S. application Ser. No. 12/819,977, filed Jun.21, 2010; which claims the benefit of U.S. Provisional Application No.61/219,326, filed Jun. 22, 2009, U.S. Provisional Application No.61/234,217, filed Aug. 14, 2009, and U.S. Provisional Application No.61/302,854, filed Feb. 9, 2010. The entireties of U.S. application Ser.Nos. 13/246,644, 12/949,660, 12/819,977 and U.S. ProvisionalApplications Nos. 61/302,854, 61/219,326, and 61/234,217 areincorporated by reference herein.

BACKGROUND

The present invention relates generally to the field of buildingmanagement systems. The present invention more particularly relates tosystems and methods for statistical control and fault detection in abuilding management system. The present invention also relates tomodel-based analysis of chillers in a building. The present inventionalso relates to user interfaces for building management systems.

SUMMARY

One embodiment of the invention relates to a controller for presentingan estimate of fault cost in a building management system. Thecontroller includes a processing circuit configured to maintain ahistory of energy usage values for the building management system and atleast one threshold parameter relative to the history of energy usagevalues. The processing circuit is configured to identify energy outlierdays by comparing energy use for a day to the history of energy usagevalues and the at least one threshold parameters. The processing circuitis further configured to subtract an estimated average daily cost,calculated with the outlier days, to an estimated average daily costcalculated excluding the outlier days to estimate a cost of the energyoutlier days. The processing circuit assigns the estimated cost of theenergy outlier days to the estimate of the fault cost in the buildingmanagement system.

Another embodiment of the invention relates to a computerized method fordetecting chiller faults. The method includes comparing an actualperformance of a chiller to an expected performance of the chiller. Themethod further includes applying a statistical analysis to thecomparison to identify a significant deviation from the expectedperformance. The method also includes generating an output signalindicative of a chiller fault in response to the identification of thesignificant deviation from the expected performance.

Alternative exemplary embodiments relate to other features andcombinations of features as may be generally recited in the claims.

BRIEF DESCRIPTION OF THE FIGURES

The disclosure will become more fully understood from the followingdetailed description, taken in conjunction with the accompanyingfigures, wherein like reference numerals refer to like elements, inwhich:

FIG. 1A is a block diagram of a building manager connected to a smartgrid and a plurality of building subsystems, according to an exemplaryembodiment;

FIG. 1B is a more detailed block diagram of the building manager shownin FIG. 1A, according to an exemplary embodiment;

FIG. 2 is a block diagram of the building subsystem integration layershown in FIG. 1A, according to an exemplary embodiment;

FIG. 3 is a detailed diagram of a portion of a smart building manager asshown in FIGS. 1A and 1B, according to an exemplary embodiment;

FIG. 4 is a detailed diagram of a fault detection and diagnostics layeras shown in FIGS. 1A and 1B, according to an exemplary embodiment; and

FIG. 5A is a flow diagram of a process for using statistical processcontrol with moving averages, according to an exemplary embodiment;

FIG. 5B is a detailed diagram of a fault detection module, according toan exemplary embodiment;

FIG. 6A is a flow diagram of a process for generating a statisticalprocess control chart, according to an exemplary embodiment;

FIG. 6B is a more detailed flow diagram of a process for generating astatistical process control chart, according to an exemplary embodiment;

FIG. 7 is a detailed diagram of a training module for generating astatistical model, according to an exemplary embodiment;

FIG. 8 is a process for measuring and verifying energy savings in abuilding management system, according to an exemplary embodiment;

FIG. 9 is a detailed diagram of a building management system usingstatistical control, according to an exemplary embodiment;

FIG. 10 is a flow diagram of a process for the identification of energyoutliers, according to an exemplary embodiment;

FIG. 11 is a flow diagram of a process for monetizing the potentialfinancial impact of energy outliers and filtering the outliers,according to an exemplary embodiment;

FIG. 12 is a flow diagram of a process for applying weather data inparallel to energy meter data for identifying energy outliers, accordingto an exemplary embodiment;

FIG. 13 is a flow diagram of a process for applying the an outlieranalysis to a building automation system for real-time monitoring ofenergy usage, according to an exemplary embodiment;

FIG. 14 is a flow chart of a process for extending the process of FIG.13 to include proactive monitoring, according to an exemplaryembodiment;

FIG. 15 is a diagram of a system for monitoring the performance of awater-cooled chiller, according to an exemplary embodiment;

FIG. 16 is a block diagram of a fault detection module and faultanalysis module, according to an exemplary embodiment;

FIG. 17A is a graphical user interface for displaying detected chillerfaults, according to an exemplary embodiment;

FIG. 17B is a graphical user interface for displaying detected chillerparameters and faults in a building, according to an exemplaryembodiment;

FIG. 17C is a graphical user interface for displaying detected chillerparameters and faults in a building, according to an exemplaryembodiment;

FIG. 17D is a graphical user interface for displaying detected chillerparameters and faults in a building, according to an exemplaryembodiment; and

FIG. 17E is a graphical user interface for displaying detected chillerparameters and faults in a building, according to an exemplaryembodiment.

DETAILED DESCRIPTION OF THE EXEMPLARY EMBODIMENTS

The invention relates to a building management system configured toimprove building efficiency, to enable greater or improved use ofrenewable energy sources, and to provide more comfortable and productivebuildings.

A building management system (BMS) is, in general, hardware and/orsoftware configured to control, monitor, and manage devices in or arounda building or building area. BMS subsystems or devices can includeheating, ventilation, and air conditioning (HVAC) subsystems or devices,security subsystems or devices, lighting subsystems or devices, firealerting subsystems or devices, elevator subsystems or devices, otherdevices that are capable of managing building functions, or anycombination thereof.

Referring now to FIG. 1A, a block diagram of a system 100 including asmart building manager 106 is shown, according to an exemplaryembodiment. Smart building manager 106 is connected to a smart grid 104and a plurality of building subsystems 128. The building subsystems 128may include a building electrical subsystem 134, an informationcommunication technology (ICT) subsystem 136, a security subsystem 138,a HVAC subsystem 140, a lighting subsystem 142, a lift/escalatorssubsystem 132, and a fire safety subsystem 130. The building subsystems128 can include fewer, additional, or alternative subsystems. Forexample, building subsystems 128 may also or alternatively include arefrigeration subsystem, an advertising or signage subsystem, a cookingsubsystem, a vending subsystem, or a printer or copy service subsystem.Conventionally, these systems are autonomous and managed by separatecontrol systems. The smart building manager 106 described herein isconfigured to achieve energy consumption and energy demand reductions byintegrating the management of the building subsystems.

Each of building subsystems 128 includes any number of devices,controllers, and connections for completing its individual functions andcontrol activities. For example, HVAC subsystem 140 may include achiller, a boiler, any number of air handling units, economizers, fieldcontrollers, supervisory controllers, actuators, temperature sensors,and other devices for controlling the temperature within a building. Asanother example, lighting subsystem 142 may include any number of lightfixtures, ballasts, lighting sensors, dimmers, or other devicesconfigured to controllably adjust the amount of light provided to abuilding space. Security subsystem 138 may include occupancy sensors,video surveillance cameras, digital video recorders, video processingservers, intrusion detection devices, access control devices andservers, or other security-related devices.

In an exemplary embodiment, the smart building manager 106 is configuredto include a communications interface 107 to the smart grid 104 outsidethe building, an interface 109 to disparate subsystems 128 within abuilding (e.g., HVAC, lighting security, lifts, power distribution,business, etc.), and an interface to applications 120, 124 (network orlocal) for allowing user control and the monitoring and adjustment ofthe smart building manager 106 or subsystems 128. Enterprise controlapplications 124 may be configured to provide subsystem-spanning controlto a graphical user interface (GUI) or to any number of enterprise-levelbusiness applications (e.g., accounting systems, user identificationsystems, etc.). Enterprise control applications 124 may also oralternatively be configured to provide configuration GUIs forconfiguring the smart building manager 106. In yet other embodiments,enterprise control applications 124 can work with layers 110-118 tooptimize building performance (e.g., efficiency, energy use, comfort, orsafety) based on inputs received at the interface 107 to the smart gridand the interface 109 to building subsystems 128. In an exemplaryembodiment, smart building manager 106 is integrated within a singlecomputer (e.g., one server, one housing, etc.). In various otherexemplary embodiments the smart building manager 106 can be distributedacross multiple servers or computers (e.g., that can exist indistributed locations).

FIG. 1B illustrates a more detailed view of smart building manager 106,according to an exemplary embodiment. In particular, FIG. 1B illustratessmart building manager 106 as having a processing circuit 152.Processing circuit 152 is shown to include a processor 154 and memorydevice 156. Processor 154 can be implemented as a general purposeprocessor, an application specific integrated circuit (ASIC), one ormore field programmable gate arrays (FPGAs), a group of processingcomponents, or other suitable electronic processing components. Memorydevice 156 (e.g., memory, memory unit, storage device, etc.) is one ormore devices (e.g., RAM, ROM, Flash memory, hard disk storage, etc.) forstoring data and/or computer code for completing or facilitating thevarious processes, layers and modules described in the presentapplication. Memory device 156 may be or include volatile memory ornon-volatile memory. Memory device 156 may include database components,object code components, script components, or any other type ofinformation structure for supporting the various activities andinformation structures described in the present application. Accordingto an exemplary embodiment, memory device 156 is communicably connectedto processor 154 via processing circuit 152 and includes computer codefor executing (e.g., by processing circuit 152 and/or processor 154) oneor more processes described herein.

Communications interfaces 107, 109 can be or include wired or wirelessinterfaces (e.g., jacks, antennas, transmitters, receivers,transceivers, wire terminals, etc.) for conducting data communicationswith, e.g., smart grid 104, energy providers and purchasers 102,building subsystems 128, or other external sources via a directconnection or a network connection (e.g., an Internet connection, a LAN,WAN, or WLAN connection, etc.). For example, communications interfaces107, 109 can include an Ethernet card and port for sending and receivingdata via an Ethernet-based communications link or network. In anotherexample, communications interfaces 107, 109 can include a WiFitransceiver for communicating via a wireless communications network. Inanother example, one or both of interfaces 107, 109 may include cellularor mobile phone communications transceivers. In one embodiment,communications interface 107 is a power line communications interfaceand communications interface 109 is an Ethernet interface. In otherembodiments, both communications interface 107 and communicationsinterface 109 are Ethernet interfaces or are the same Ethernetinterface. Further, while FIG. 1A shows applications 120 and 124 asexisting outside of smart building manager 106, in some embodiments,applications 120 and 124 may be hosted within smart building manager 106generally or memory device 156 more particularly.

Building Subsystem Integration Layer

Referring further to FIG. 1B, the building subsystem integration layer118 is configured to manage communications between the rest of the smartbuilding manager 106's components and the building subsystems. Thebuilding subsystem integration layer 118 may also be configured tomanage communications between building subsystems. The buildingsubsystem integration layer 118 may be configured to translatecommunications (e.g., sensor data, input signals, output signals, etc.)across a plurality of multi-vendor/multi-protocol systems. For example,the building subsystem integration layer 118 may be configured tointegrate data from subsystems 128.

In FIG. 2, the building subsystem integration layer 118 is shown ingreater detail to include a message format and content normalizationcomponent 202. The message format and content normalization component202 is configured to convert data messages for and from disparatelyprotocolled devices or networks (e.g., different building subsystems,differently protocolled smart-grid sources, etc.). The message formatand content normalization component 202 is shown to include twosubcomponents, an application normalization component 204 and a buildingsubsystem normalization component 206. The application normalizationcomponent 204 is a computer function, object, service, or combinationthereof configured to drive the conversion of communications for andfrom applications (e.g., enterprise level applications 120, 124 shown inFIG. 1A, a computerized maintenance management system 222, utilitycompany applications via smart grid 104 shown in FIG. 1A, etc.). Thebuilding subsystem normalization component 206 is a computer function,object, service, or combination thereof configured to drive theconversion of communications for and from building subsystems (e.g.,building subsystems 128 shown in FIG. 1A, building subsystemcontrollers, building devices, security systems, fire systems, etc.).The application normalization component 204 and the building subsystemnormalization component 206 are configured to accommodate multiplecommunications or data protocols. In some embodiments, the applicationnormalization component 204 and the building subsystem normalizationcomponent 206 are configured to conduct the conversion for each protocolbased on information stored in modules 208-220 (e.g., a table, a script,in memory device 156 shown in FIG. 1B) for each of systems or devices222-234. The protocol modules 208-220 may be, for example, schema mapsor other descriptions of how a message for one protocol should betranslated to a message for a second protocol. In some embodiments themodules 208-220 may be “plug-in” drivers that can be easily installed toor removed from a building subsystem integration layer 118 (e.g., via anexecutable installation routine, by placing a file in an interfacesfolder, etc.) during setup. For example, modules 208-220 may be vendorspecific (e.g., Johnson Controls, Honeywell, Siemens, etc.),standards-based (e.g., BACnet, ANSI C12.19, Lon Works, Modbus, RIP,SNMP, SOAP, web services, HTML, HTTP/HTTPS, XML, XAML, TFTP, DHCP, DNS,SMTP, SNTP, etc.), user built, user selected, or user customized. Insome embodiments the application normalization component 204 or buildingsubsystem normalization component 206 are configured for compatibilitywith new modules or drivers (e.g., user defined or provided by a vendoror third party). In such embodiments, message format and contentnormalization component 202 may advantageously be scaled for futureapplications or case-specific requirements (e.g., situations calling forthe use of additional cyber security standards such as dataencryption/decryption) by changing the active module set or byinstalling a new module.

Using message format and content normalization component 202, thebuilding subsystem integration layer 118 can be configured to provide aservice-oriented architecture for providing cross-subsystem controlactivities and cross-subsystem applications. The message format andcontent normalization component 202 can be configured to provide arelatively small number of straightforward interfaces (e.g., applicationprogramming interfaces (APIs)) or protocols (e.g., open protocols,unified protocols, common protocols) for use by layers 108-116 (shown inFIG. 1A) or external applications (e.g., 120, 124 shown in FIG. 1A) andto “hide” such layers or applications from the complexities of theunderlying subsystems and their particular data transport protocols,data formats, semantics, interaction styles, and the like. Configurationof the message format and content normalization component 202 may occurautomatically (e.g., via a building subsystem and device discoveryprocess), via user configuration, or by a combination of automateddiscovery and user configuration. User configuration may be driven byproviding one or more graphical user interfaces or “wizards” to a user,the graphical user interfaces allowing the user to map an attribute fromone protocol to an attribute of another protocol. Configuration tool 162shown in FIG. 1B may be configured to drive such an association process.The configuration tool 162 may be served to clients (local or remote)via web services 158 and/or GUI engine 160 (both shown in FIG. 1B). Theconfiguration tool 162 may be provided as a thin web client (e.g., thatprimarily interfaces with web services 158) or a thick client (e.g.,that only occasionally draws upon web services 158 and/or GUI engine160). Configuration tool 162 may be configured to use a W3C standardintended to harmonize semantic information from different systems tocontrollably define, describe and store relationships between thedata/protocols (e.g., define the modules 208-220). For example, the W3Cstandard used may be the Web Ontology Language (OWL). In some exemplaryembodiments, configuration tool 162 may be configured to prepare themessage format and content normalization component 202 (anddevice/protocol modules 208-220 thereof) for machine levelinteroperability of data content.

Once the building subsystem integration layer 118 is configured,developers of applications may be provided with a software developmentkit to allow rapid development of applications compatible with the smartbuilding manager (e.g., with an application-facing protocol or API ofthe building subsystem integration layer). Such an API orapplication-facing protocol may be exposed at the enterprise integrationlayer 108 shown in FIGS. 1A and 1B. In various exemplary embodiments,the smart building manager 106 including building subsystem integrationlayer 118 includes the following features or advantages: it is seamlessin that heterogeneous applications and subsystems may be integratedwithout varying or affecting the behavior of the external facinginterfaces or logic; it is open in that it allows venders to developproducts and applications by coding adapters (e.g. modules 208-220 shownin FIG. 2) or features according to a well-defined specification; it ismulti-standard in that it supports subsystems that operate according tostandards as well as proprietary protocols; it is extensible in that itaccommodates new applications and subsystems with little to nomodification; it is scalable in that it supports many applications andsubsystems; it is adaptable in that it allows for the addition ordeletion of applications or subsystems without affecting systemconsistency; it is user-configurable in that it is adjustable to changesin the business environment, business rules, or business workflows; andit is secure in that it protects information transferred through theintegration channel. Additional details with respect to buildingsubsystem integration layer 118 are described below with respect to FIG.3.

Integrated Control Layer

Referring further to FIGS. 1A and 1B, the integrated control layer 116is configured to use the data input or output of the building subsystemintegration layer 118 to make control decisions. Due to the subsystemintegration provided by the building subsystem integration layer 118,the integrated control layer 116 can integrate control activities of thesubsystems 128 such that the subsystems 128 behave as a singleintegrated supersystem. In an exemplary embodiment, the integratedcontrol layer 116 includes control logic that uses inputs and outputsfrom a plurality of building subsystems to provide greater comfort andenergy savings relative to the comfort and energy savings that separatesubsystems could provide alone. For example, information from a firstbuilding subsystem may be used to control a second building subsystem.By way of a more particular example, when a building employee badges inat a parking garage, a message may be sent from the parking subsystem tothe building subsystem integration layer 118, converted into an eventrecognized as a universal occupancy (e.g., “badge-in”) event andprovided to integrated control layer 116. Integrated control layer 116may include logic that turns on the lights in the building employee'soffice, begins cooling the building employee's office in response to theanticipated occupancy, and boots up the employee's computer. Thedecision to turn the devices on is made by integrated control layer 116and integrated control layer 116 may cause proper “on” commands to beforwarded to the particular subsystems (e.g., the lighting subsystem,the IT subsystem, the HVAC subsystem). The integrated control layer 116passes the “on” commands through building subsystem integration layer118 so that the messages are properly formatted or protocolled forreceipt and action by the subsystems. As is illustrated in FIGS. 1A-B,the integrated control layer 116 is logically above the buildingsubsystems and building subsystem controllers. The integrated controllayer 116, by having access to information from multiple systems, isconfigured to use inputs from one or more building subsystems 128 tomake control decisions for control algorithms of other buildingsubsystems. For example, the “badge-in” event described above can beused by the integrated control layer 116 (e.g., a control algorithmthereof) to provide new setpoints to an HVAC control algorithm of theHVAC subsystem.

While conventional building subsystem controllers are only able toprocess inputs that are directly relevant to the performance of theirown control loops, the integrated control layer 116 is configured to usean input from a first subsystem to make an energy-saving controldecision for a second subsystem. Results of these decisions can becommunicated back to the building subsystem integration layer 116 via,for example, the message format and content normalization component 202shown in FIG. 2A. Therefore, advantageously, regardless of theparticular HVAC system or systems connected to the smart buildingmanager, and due to the normalization at the building subsystemintegration layer 118, the integrated control layer's control algorithmscan determine a control strategy using normalized temperature inputs,and provide an output including a normalized setpoint temperature to thebuilding subsystem integration layer. The building subsystem integrationlayer 118 can translate the normalized setpoint temperature into acommand specific to the building subsystem or controller for which thesetpoint adjustment is intended. If multiple subsystems are utilized tocomplete the same function (e.g., if multiple disparately protocolledHVAC subsystems are provided in different regions of a building), thebuilding subsystem integration layer 118 can convert a command decision(e.g., to lower the temperature setpoint by 2 degrees) to multipledifferent commands for receipt and action by the multiple disparatelyprotocolled HVAC subsystems. In this way, functions of the integratedcontrol layer 116 may be executed using the capabilities of buildingsubsystem integration layer 118. In an exemplary embodiment, theintegrated control layer is configured to conduct the primary monitoringof system and subsystem statuses and interrelationships for thebuilding. Such monitoring can cross the major energy consumingsubsystems of a building to allow for cross-subsystem energy savings tobe achieved (e.g., by the demand response layer 112).

The integrated control layer 116 is shown to be logically below thedemand response layer 112. The integrated control layer 116 isconfigured to enhance the effectiveness of the demand response layer 112by enabling building subsystems 128 and their respective control loopsto be controlled in coordination with the demand response layer 112.This configuration may advantageously reduce disruptive demand responsebehavior relative to conventional systems. For example, the integratedcontrol layer 116 may be configured to assure that a demandresponse-driven upward adjustment to the setpoint for chilled watertemperature (or another component that directly or indirectly affectstemperature) does not result in an increase in fan energy (or otherenergy used to cool a space) that would result in greater total buildingenergy use than was saved at the chiller. The integrated control layer116 may also be configured to provide feedback to the demand responselayer 112 so that the demand response layer 112 checks that constraints(e.g., temperature, lighting levels, etc.) are properly maintained evenwhile demanded load shedding is in progress. The constraints may alsoinclude setpoint or sensed boundaries relating to safety, equipmentoperating limits and performance, comfort, fire codes, electrical codes,energy codes, and the like. The integrated control layer 116 is alsologically below the fault detection and diagnostics layer 114 and theautomated measurement and validation layer 110. The integrated controllayer may be configured to provide calculated inputs (e.g.,aggregations) to these “higher levels” based on outputs from more thanone building subsystem.

Control activities that may be completed by the integrated control layer116 (e.g., software modules or control algorithms thereof) includeoccupancy-based control activities. Security systems such as radiofrequency location systems (RFLS), access control systems, and videosurveillance systems can provide detailed occupancy information to theintegrated control layer 116 and other building subsystems 128 via thesmart building manager 106 (and more particularly, via the buildingsubsystem integration layer 118). Integration of an access controlsubsystem and a security subsystem for a building may provide detailedoccupancy data for consumption by the integrated control layer 116(e.g., beyond binary “occupied” or “unoccupied” data available to someconventional HVAC systems that rely on, for example, a motion sensor).For example, the exact number of occupants in the building (or buildingzone, floor, conference room, etc.) may be provided to the integratedcontrol layer 116 or aggregated by the integrated control layer 116using inputs from a plurality of subsystems. The exact number ofoccupants in the building can be used by the integrated control layer116 to determine and command appropriate adjustments for buildingsubsystems 128 (such as HVAC subsystem 140 or lighting subsystem 142).Integrated control layer 116 may be configured to use the number ofoccupants, for example, to determine how many of the available elevatorsto activate in a building. If the building is only 20% occupied, theintegrated control layer 116, for example, may be configured to powerdown 80% of the available elevators for energy savings. Further,occupancy data may be associated with individual workspaces (e.g.,cubicles, offices, desks, workstations, etc.) and if a workspace isdetermined to be unoccupied by the integrated control layer, a controlalgorithm of the integrated control layer 116 may allow for the energyusing devices serving the workspace to be turned off or commanded toenter a low power mode. For example, workspace plug-loads, tasklighting, computers, and even phone circuits may be affected based on adetermination by the integrated control layer that the employeeassociated with the workspace is on vacation (e.g., using data inputsreceived from a human-resources subsystem). Significant electrical loadsmay be shed by the integrated control layer 116, including, for example,heating and humidification loads, cooling and dehumidification loads,ventilation and fan loads, electric lighting and plug loads (e.g. withsecondary thermal loads), electric elevator loads, and the like. Theintegrated control layer 116 may further be configured to integrate anHVAC subsystem or a lighting subsystem with sunlight shading devices orother “smart window” technologies. Natural day-lighting cansignificantly offset lighting loads but for optimal comfort may becontrolled by the integrated control layer to prevent glare orover-lighting. Conversely, shading devices and smart windows may also becontrolled by the integrated control layer 116 to calculably reducesolar heat gains in a building space, which can have a significantimpact on cooling loads. Using feedback from sensors in the space, andwith knowledge of the HVAC control strategy, the integrated controllayer 116 may further be configured to control the transmission ofinfrared radiation into the building, minimizing thermal transmissionwhen the HVAC subsystem is cooling and maximizing thermal transmissionwhen the HVAC subsystem is heating. As a further example of anoccupancy-based control strategy that may be implemented by theintegrated control layer 116, inputs from a video security subsystem maybe analyzed by a control algorithm of the integrated control layer 116to make a determination regarding occupancy of a building space. Usingthe determination, the control algorithm may turn off the lights, adjustHVAC set points, power-down ICT devices serving the space, reduceventilation, and the like, enabling energy savings with an acceptableloss of comfort to occupants of the building space.

Referring now to FIG. 3, a detailed diagram of a portion of smartbuilding manager 106 is shown, according to an exemplary embodiment. Inparticular, FIG. 3 illustrates a detailed embodiment of integratedcontrol layer 116. Configuration tools 162 can allow a user to define(e.g., via graphical user interfaces, via prompt-driven “wizards,” etc.)how the integrated control layer 116 should react to changing conditionsin the building subsystems 128. In an exemplary embodiment,configuration tools 162 allow a user to build and storecondition-response scenarios that can cross multiple building subsystemsand multiple enterprise control applications (e.g., work ordermanagement system applications, entity resource planning (ERP)applications, etc.).

Building subsystems 128, external sources such as smart grid 104, andinternal layers such as demand response layer 112 can regularly generateevents (e.g., messages, alarms, changed values, etc.) and provide theevents to integrated control layer 116 or another layer configured tohandle the particular event. For example, demand response (DR) events(e.g., a change in real time energy pricing) may be provided to smartbuilding manager 106 as Open Automated Demand Response (“OpenADR”)messages (a protocol developed by Lawrence Berkeley NationalLaboratories). The DR messages may be received by OpenADR adapter 306(which may be a part of enterprise application layer 108 shown in FIGS.1A and 1B). The OpenADR adapter 306 may be configured to convert theOpenADR message into a DR event configured to be understood (e.g.,parsed, interpreted, processed, etc.) by demand response layer 112. TheDR event may be formatted and transmitted according to or via a servicebus 302 for the smart building manager 106.

Service bus adapter 304 may be configured to “trap” or otherwise receivethe DR event on the service bus 302 and forward the DR event on todemand response layer 112. Service bus adapter 304 may be configured toqueue, mediate, or otherwise manage demand response messages for demandresponse layer 112. Once a DR event is received by demand response layer112, logic thereof can generate a control trigger in response toprocessing the DR event. The integrated control engine 308 of integratedcontrol layer 116 is configured to parse the received control trigger todetermine if a control strategy exists in control strategy database 310that corresponds to the received control trigger. If a control strategyexists, integrated control engine 308 executes the stored controlstrategy for the control trigger. In some cases the output of theintegrated control engine 308 will be an “apply policy” message forbusiness rules engine 312 to process. Business rules engine 312 mayprocess an “apply policy” message by looking up the policy in businessrules database 313. A policy in business rules database 313 may take theform of a set of action commands for sending to building subsystems 128.The set of action commands may include ordering or scripting forconducting the action commands at the correct timing, ordering, or withother particular parameters. When business rules engine 312 processesthe set of action commands, therefore, it can control the ordering,scripting, and other parameters of action commands transmitted to thebuilding subsystems 128.

Action commands may be commands for relatively direct consumption bybuilding subsystems 128, commands for other applications to process, orrelatively abstract cross-subsystem commands. Commands for relativelydirect consumption by building subsystems 128 can be passed throughservice bus adapter 322 to service bus 302 and to a subsystem adapter314 for providing to a building subsystem in a format particular to thebuilding subsystem. Commands for other applications to process mayinclude commands for a user interface application to request feedbackfrom a user, a command to generate a work order via a computerizedmaintenance management system (CMMS) application, a command to generatea change in an ERP application, or other application level commands.

More abstract cross-subsystem commands may be passed to a semanticmediator 316 which performs the task of translating those actions to thespecific commands required by the various building subsystems 128. Forexample, a policy might contain an abstract action to “set lighting zoneX to maximum light.” The semantic mediator 316 may translate this actionto a first command such as “set level to 100% for lighting object O incontroller C” and a second command of “set lights to on in controller Z,zone_id_no 3141593.” In this example both lighting object O incontroller C and zone_id_no 3141593 in controller Z may affect lightingin zone X. Controller C may be a dimming controller for accent lightingwhile controller Z may be a non-dimming controller for the primarylighting in the room. The semantic mediator 316 is configured todetermine the controllers that relate to zone X using ontology database320. Ontology database 320 stores a representation or representations ofrelationships (the ontology) between building spaces and subsystemelements and subsystems elements and concepts of the integrated buildingsupersystem. Using the ontology stored in ontology database 320, thesemantic mediator can also determine that controller C is dimming andrequires a numerical percentage parameter while controller Z is notdimming and requires only an on or off command. Configuration tool 162can allow a user to build the ontology of ontology database 320 byestablishing relationships between subsystems, building spaces,input/output points, or other concepts/objects of the buildingsubsystems and the building space.

Events other than those received via OpenADR adapter 306, demandresponse layer 112, or any other specific event-handing mechanism can betrapped by subsystem adapter 314 (a part of building integrationsubsystem layer 318) and provided to a general event manager 330 viaservice bus 302 and a service bus adapter. By the time an event from abuilding subsystem 128 is received by event manager 330, it may havebeen converted into a unified event (i.e., “common event,” “standardizedevent”, etc.) by subsystem adapter 314 and/or other components ofbuilding subsystem integration layer 318 such as semantic mediator 316.The event manager 330 can utilize an event logic DB to lookup controltriggers, control trigger scripts, or control trigger sequences based onreceived unified events. Event manager 330 can provide control triggersto integrated control engine 308 as described above with respect todemand response layer 112. As events are received, they may be archivedin event history 332 by event manager 330. Similarly, demand responselayer 112 can store DR events in DR history 335. One or both of eventmanager 330 and demand response layer 112 may be configured to waituntil multi-event conditions are met (e.g., by processing data inhistory as new events are received). For example, demand response layer112 may include logic that does not act to reduce energy loads until aseries of two sequential energy price increases are received. In anexemplary embodiment event manager 330 may be configured to receive timeevents (e.g., from a calendaring system). Different time events can beassociated with different triggers in event logic database 333.

In an exemplary embodiment, the configuration tools 162 can be used tobuild event conditions or trigger conditions in event logic 333 orcontrol strategy database 310. For example, the configuration tools 162can provide the user with the ability to combine data (e.g., fromsubsystems, from event histories) using a variety of conditional logic.In varying exemplary embodiments, the conditional logic can range fromsimple logical operators between conditions (e.g., AND, OR, XOR, etc.)to pseudo-code constructs or complex programming language functions(allowing for more complex interactions, conditional statements, loops,etc.). The configuration tools 162 can present user interfaces forbuilding such conditional logic. The user interfaces may allow users todefine policies and responses graphically. In some embodiments, the userinterfaces may allow a user to select a pre-stored or pre-constructedpolicy and adapt it or enable it for use with their system.

Referring still to FIG. 3, in some embodiments, integrated control layer116 generally and integrated control engine 308 can operate as a“service” that can be used by higher level layers of smart buildingmanager 106, enterprise applications, or subsystem logic whenever apolicy or sequence of actions based on the occurrence of a condition isto be performed. In such embodiments, control operations do not need tobe reprogrammed Instead, applications or logic can rely on theintegrated control layer 116 to receive an event and to execute therelated subsystem functions. For example, demand response layer 112,fault detection and diagnostics layer 114 (shown in FIGS. 1A and 1B),enterprise integration 108, and applications 120, 124 may all utilize ashared control strategy 310 and integrated control engine 308 toinitiate response sequences to events.

Fault Detection and Diagnostics Layer

Referring now to FIG. 4, the fault detection and diagnostics (FDD) layer114 is shown in greater detail, according to an exemplary embodiment.Fault detection and diagnostics (FDD) layer 114 is configured to provideon-going fault detection of building subsystems, building subsystemdevices, and control algorithms of the integrated control layer. The FDDlayer 114 may receive its inputs from the integrated control layer,directly from one or more building subsystems or devices, or from thesmart grid. The FDD layer 114 may automatically diagnose and respond todetected faults. The responses to detected or diagnosed faults mayinclude providing an alert message to a user, a maintenance schedulingsystem, or a control algorithm configured to attempt to repair the faultor to work-around the fault. In other exemplary embodiments FDD layer114 is configured to provide “fault” events to integrated control layeras described with reference to FIG. 3 and the integrated control layerof FIG. 3 is configured to execute control strategies and policies inresponse to the received fault events. According to an exemplaryembodiment, the FDD layer 114 (or a policy executed by an integratedcontrol engine or business rules engine) may shut-down systems or directcontrol activities around faulty devices or systems to reduce energywaste, extend equipment life, or assure proper control response. The FDDlayer 114 may be configured to use statistical analysis of nearreal-time or historical building subsystem data to rapidly identifyfaults in equipment operation.

As shown in FIG. 4, the FDD layer 114 is configured to store or access avariety of different system data stores (or data points for live data)402-410. FDD layer 114 may use some content of data stores 402-410 toidentify faults at the equipment level (e.g., specific chiller, specificAHU, specific terminal unit, etc.) and other content to identify faultsat component or subsystem levels. The FDD layer 114 may be configured tooutput a specific identification of the faulty component or cause of thefault (e.g., loose damper linkage) using detailed subsystem inputsavailable at the building subsystem integration layer (shown in previousFigures). Such specificity and determinations may be calculated by theFDD layer 114 based on such subsystem inputs and, for example, automatedfault detection module 412. Automated fault detection module 412 canutilize pattern recognition methods, pattern classification methods,rule-based classification methods, outlier analysis, statistical qualitycontrol charting techniques, or the like to conduct its statisticalanalysis. In some embodiments automated fault detection module 412 moreparticularly is configured to calculate or update performance indices410.

Performance indices 410 may be calculated based onexponentially-weighted moving averages (EWMAs) to provide statisticalanalysis features which allow outlier and statistical process control(SPC) techniques to be used to identify faults. For example, the FDDlayer 114 may be configured to use meter data 402 outliers to detectwhen energy consumption becomes abnormal. Statistical fault detectionmodule 412 may also or alternatively be configured to analyze the meterdata 402 using statistical methods that provide for data clustering,outlier analysis, or quality control determinations. The meter data 402may be received from, for example, a smart meter, a utility, orcalculated based on the building-use data available to the smartbuilding manager.

Once a fault is detected by the FDD layer 114 (e.g., by statisticalfault detection module 412), the FDD layer 114 may be configured togenerate one or more alarms or events to prompt manual fault diagnosticsor to initiate an automatic fault diagnostics activity via automateddiagnostics module 414. Automatic fault diagnostics module 414 may beconfigured to use meter data 402, weather data 404, model data 406(e.g., performance models based on historical building equipmentperformance), building subsystem data 408, performance indices 410, orother data available at the building subsystem integration layer tocomplete its fault diagnostics activities.

In an exemplary embodiment, when a fault is detected, the automateddiagnostics module 414 is configured to investigate the fault byinitiating expanded data logging and error detection/diagnosticsactivities relative to the inputs, outputs, and systems related to thefault. For example, the automated diagnostics module 414 may beconfigured to poll sensors associated with an air handling unit (AHU)(e.g., temperature sensors for the space served by the AHU, air flowsensors, position sensors, etc.) on a frequent or more synchronizedbasis to better diagnose the source of a detected AHU fault.

Automated fault diagnostics module 414 may further be configured tocompute residuals (differences between measured and expected values) foranalysis to determine the fault source. For example, automated faultdiagnostics module 414 may be configured to implement processingcircuits or methods described in U.S. patent application Ser. No.12/487,594, filed Jun. 18, 2009, titled “Systems and Methods for FaultDetection of Air Handling Units,” the entirety of which is incorporatedherein by reference. Automated fault diagnostics module 414 can use afinite state machine and input from system sensors (e.g., temperaturesensors, air mass sensors, etc.) to diagnose faults. State transitionfrequency (e.g., between a heating state, a free cooling state, and amechanical cooling state) may also be used by the statistical faultdetection module 412 and the automated diagnostics module 414 toidentify and diagnose unstable control issues. The FDD layer 114 mayalso or alternatively be configured for rule-based predictive detectionand diagnostics (e.g., to determine rule thresholds, to provide forcontinuous monitoring and diagnostics of building equipment).

In addition to or as an alternative to an automated diagnostics processprovided by automated diagnostics module 414, FDD layer 114 can drive auser through a manual diagnostic process using manual diagnostics module416. One or both of automated diagnostics module 414 and manualdiagnostics module 416 can store data regarding the fault and thediagnosis thereof for further assessment by manual or automated faultassessment engine 418. Any manually driven process of assessment engine418 can utilize graphical or textual user interfaces displayed to a userto receive feedback or input from a user. In some embodiments assessmentengine 418 will provide a number of possible reasons for a fault to theuser via a GUI. The user may select one of the faults for manualinvestigation or calculation. Similarly, an automated process ofassessment engine 418 may be configured to select the most probablecause for a fault based on diagnostics provided by modules 414 or 416.Once a cause is detected or estimated using assessment engine 418, awork order can be generated by work order generation and dispatchservice 420. Work order generation and dispatch service can transmit thework order to a service management system or a work dispatch service 420for action.

Data and processing results from modules 412, 414, 416, 418 or otherdata stored or modules of a fault detection and diagnostics layer can beprovided to the enterprise integration layer shown in FIGS. 1A and 1B.Monitoring and reporting applications 120 can then access the data or bepushed the data so that real time “system health” dashboards can beviewed and navigated by a user (e.g., a building engineer). For example,monitoring and reporting applications 120 may include a web-basedmonitoring application that includes several graphical user interface(GUI) elements (e.g., widgets, dashboard controls, windows, etc.) fordisplaying key performance indicators (KPI) or other information tousers of a GUI using FDD layer 114 information or analyses. In addition,the GUI elements may summarize relative energy use and intensity acrossdifferent buildings (real or modeled), different campuses, or the like.Other GUI elements or reports may be generated and shown based onavailable data that allow facility managers to assess performance acrossa group of buildings from one screen. The user interface or report (orunderlying data engine) may be configured to aggregate and categorizefaults by building, building type, equipment type, fault type, times ofoccurrence, frequency of occurrence, severity, and the like. The GUIelements may include charts or histograms that allow the user tovisually analyze the magnitude of occurrence of specific faults orequipment for a building, time frame, or other grouping. A “time series”pane of the GUI may allow users to diagnose a fault remotely byanalyzing and comparing interval time-series data, trends, and patternsfor various input/output points tracked/logged by the FDD layer 114. TheFDD layer 114 may include one or more GUI servers or services 422 (e.g.,a web service) to support such applications. Further, in someembodiments, applications and GUI engines may be included outside of theFDD layer 114 (e.g., monitoring and reporting applications 120 shown inFIG. 1A, web services 158 shown in FIG. 1B, GUI engine 160 shown in FIG.1B). The FDD layer 114 may be configured to maintain detailed historicaldatabases (e.g., relational databases, XML databases, etc.) of relevantdata and includes computer code modules that continuously, frequently,or infrequently query, aggregate, transform, search, or otherwiseprocess the data maintained in the detailed databases. The FDD layer 114may be configured to provide the results of any such processing to otherdatabases, tables, XML files, or other data structures for furtherquerying, calculation, or access by, for example, external monitoringand reporting applications.

In an exemplary embodiment, the automated diagnostics module 414automatically prioritizes detected faults. The prioritization may beconducted based on customer-defined criteria. The prioritization may beused by the manual or automated fault assessment module 418 to determinewhich faults to communicate to a human user via a dashboard or otherGUI. Further, the prioritization can be used by the work order dispatchservice to determine which faults are worthy of immediate investigationor which faults should be investigated during regular servicing ratherthan a special work request. The FDD layer 114 may be configured todetermine the prioritization based on the expected financial impact ofthe fault. The fault assessment module 418 may retrieve faultinformation and compare the fault information to historical information.Using the comparison, the fault assessment module 418 may determine anincreased energy consumption and use pricing information from the smartgrid to calculate the cost over time (e.g., cost per day). Each fault inthe system may be ranked according to cost or lost energy. The faultassessment module 418 may be configured to generate a report forsupporting operational decisions and capital requests. The report mayinclude the cost of allowing faults to persist, energy wasted due to thefault, potential cost to fix the fault (e.g., based on a serviceschedule), or other overall metrics such as overall subsystem orbuilding reliability (e.g., compared to a benchmark). The faultassessment module 418 may further be configured to conduct equipmenthierarchy-based suppression of faults (e.g., suppressed relative to auser interface, suppressed relative to further diagnostics, etc.). Forsuch suppression, module 318 may use the hierarchical informationavailable at, e.g., integrated control layer 116 or building subsystemintegration layer 318 shown in FIG. 3. For example, module 318 mayutilize building subsystem hierarchy information stored in ontologydatabase 320 to suppress lower level faults in favor of a higher levelfault (suppress faults for a particular temperature sensor and airhandling unit in favor of a fault that communicates “Inspect HVACComponents Serving Conference Room 30”).

FDD layer 114 may also receive inputs from lower level FDD processes.For example, FDD layer 114 may receive inputs from building subsystemsupervisory controllers or field controllers having FDD features. In anexemplary embodiment, FDD layer 114 may receive “FDD events,” processthe received FDD events, query the building subsystems for furtherinformation, or otherwise use the FDD events in an overall FDD scheme(e.g., prioritization and reporting). U.S. Pat. No. 6,223,544 (titled“Integrated Control and Fault Detection of HVAC Equipment,” issued May1, 2001)(incorporated herein by reference) and U.S. Pub. No.2009/0083583 (titled “Fault Detection Systems and Methods forSelf-Optimizing Heating, Ventilation, and Air Conditioning Controls”,filed Nov. 25, 2008, published Mar. 26, 2009)(incorporated herein byreference) may be referred to as examples of FDD systems and methodsthat may be implemented by FDD layer 114 (and/or lower level FDDprocesses for providing information to FDD layer 114).

Demand Response Layer

FIGS. 1A and 1B are further shown to include a demand response (DR)layer 112. The DR layer 112 is configured to optimize electrical demandin response to time-of-use prices, curtailment signals, or energyavailability. Data regarding time-of-use prices, energy availability,and curtailment signals may be received from the smart grid 104, fromenergy providers and purchasers 102 (e.g., an energy aggregator) via thesmart grid 104, from energy providers and purchasers 102 via acommunication network apart from the smart grid, from distributed energygeneration systems 122, from energy storage banks 126, or from othersources. According to an exemplary embodiment, the DR layer 112 includescontrol logic for responding to the data and signals it receives. Theseresponses can include communicating with the control algorithms in theintegrated control layer 116 to “load shed,” changing controlstrategies, changing setpoints, or shutting down building devices orsubsystems in a controlled manner. The architecture and process forsupporting DR events is shown in and described with reference to FIG. 3.The DR layer 112 may also include control logic configured to determinewhen to utilize stored energy based on information from the smart gridand information from a local or remote energy storage system. Forexample, when the DR layer 112 receives a message indicating risingenergy prices during a future “peak use” hour, the DR layer 112 candecide to begin using power from the energy storage system just prior tothe beginning of the “peak use” hour.

In some exemplary embodiments the DR layer 112 may include a controlmodule configured to actively initiate control actions (e.g.,automatically changing setpoints) which minimize energy costs based onone or more inputs representative of or based on demand (e.g., price, acurtailment signal, a demand level, etc.). The DR layer 112 may furtherinclude or draw upon one or more DR policy definitions (e.g., databases,XML files, etc.). The policy definitions may be edited or adjusted by auser (e.g., via a graphical user interface) so that the control actionsinitiated in response to demand inputs may be tailored for the user'sapplication, desired comfort level, particular building equipment, orbased on other concerns. For example, the DR policy definitions canspecify which equipment may be turned on or off in response toparticular demand inputs, how long a system or piece of equipment shouldbe turned off, what setpoints can be changed, what the allowable setpoint adjustment range is, how long to hold a “high demand” setpointbefore returning to a normally scheduled setpoint, how close to approachcapacity limits, which equipment modes to utilize, the energy transferrates (e.g., the maximum rate, an alarm rate, other rate boundaryinformation, etc.) into and out of energy storage devices (e.g., thermalstorage tanks, battery banks, etc.), and when to dispatch on-sitegeneration of energy (e.g., via fuel cells, a motor generator set,etc.). One or more of the policies and control activities may be locatedwithin control strategy database 310 or business rules database 313.Further, as described above with reference to FIG. 3, some of the DRresponses to events may be processed and completed by integrated controllayer 116 with or without further inputs or processing by DR layer 112.

A plurality of market-based DR inputs and reliability based DR inputsmay be configured (e.g., via the DR policy definitions or other systemconfiguration mechanisms) for use by the DR layer 112. The smartbuilding manager 106 may be configured (e.g., self-configured, manuallyconfigured, configured via DR policy definitions, etc.) to select,deselect or differently weigh varying inputs in the DR layer'scalculation or execution of control strategies based on the inputs. DRlayer 112 may automatically (or via the user configuration) calculateoutputs or control strategies based on a balance of minimizing energycost and maximizing comfort. Such balance may be adjusted (e.g.,graphically, via rule sliders, etc.) by users of the smart buildingmanager via a configuration utility or administration GUI.

The DR layer 112 may be configured to receive inputs from other layers(e.g., the building subsystem integration layer, the integrated controllayer, etc.). The inputs received from other layers may includeenvironmental or sensor inputs such as temperature, carbon dioxidelevels, relative humidity levels, air quality sensor outputs, occupancysensor outputs, room schedules, and the like. The inputs may alsoinclude inputs such as electrical use (e.g., expressed in kWh), thermalload measurements, pricing information, projected pricing, smoothedpricing, curtailment signals from utilities, and the like from insidethe system, from the smart grid 104, or from other remote sources.

Some embodiments of the DR layer 112 may utilize industry standard“open” protocols or emerging National Institute of Standards andTechnology (NIST) standards to receive real-time pricing (RTP) orcurtailment signals from utilities or power retailers. In otherembodiments, proprietary protocols or other standards may be utilized.As mentioned above, in some exemplary embodiments, the DR layer 112 isconfigured to use the OpenADR protocol to receive curtailment signals orRTP data from utilities, other independent system operators (ISOs), orother smart grid sources. The DR layer 112, or another layer (e.g., theenterprise integration layer) that serves the DR layer 112 may beconfigured to use one or more security schemes or standards such as theOrganization for the Advancement of Structured Information Standards(OASIS) Web Service Security Standards to provide for securecommunications to/from the DR layer 112 and the smart grid 104 (e.g., autility company's data communications network). If the utility does notuse a standard protocol (e.g., the OpenADR protocol), the DR layer 112,the enterprise integration layer 108, or the building subsystemintegration layer 118 may be configured to translate the utility'sprotocol into a format for use by the utility. The DR layer 112 may beconfigured to bi-directionally communicate with the smart grid 104 orenergy providers and purchasers 102 (e.g., a utility, an energyretailer, a group of utilities, an energy broker, etc.) to exchangeprice information, demand information, curtailable load calculations(e.g., the amount of load calculated by the DR layer to be able to beshed without exceeding parameters defined by the system or user), loadprofile forecasts, and the like. DR layer 112 or an enterpriseapplication 120, 124 in communication with the DR layer 112 may beconfigured to continuously monitor pricing data provided byutilities/ISOs across the nation, to parse the useful information fromthe monitored data, and to display the useful information to a user toor send the information to other systems or layers (e.g., integratedcontrol layer 116).

The DR layer 112 may be configured to include one or more adjustablecontrol algorithms in addition to or as an alternative from allowing theuser creation of DR profiles. For example, one or more controlalgorithms may be automatically adjusted by the DR layer 112 usingdynamic programming or model predictive control modules. In oneembodiment, business rules engine 312 is configured to respond to a DRevent by adjusting a control algorithm or selecting a different controlalgorithm to use (e.g., for a lighting system, for an HVAC system, for acombination of multiple building subsystems, etc.).

The smart building manager 106 (e.g., using the demand response layer112) can be configured to automatically (or with the help of a user)manage energy spend. The smart building manager 106 (with input from theuser or operating using pre-configured business rules shown in FIG. 3)may be configured to accept time-of-use pricing signals or informationfrom a smart grid (e.g., an energy provider, a smart meter, etc.) and,using its knowledge of historical building system data, controlalgorithms, calendar information, and/or weather information receivedfrom a remote source, may be configured to conduct automatic costforecasting. The smart building manager 106 (e.g., the demand responselayer 112) may automatically (or with user approval) take specific loadshedding actions or control algorithm changes in response to differentcost forecasts.

The smart building manager 106 may also be configured to monitor andcontrol energy storage systems 126 (e.g., thermal, electrical, etc.) anddistributed generation systems 122 (e.g., a solar array for thebuilding, etc.). The smart building manager 106 or DR layer 112 may alsobe configured to model utility rates to make decisions for the system.All of the aforementioned processing activities or inputs may be used bythe smart building manager 106 (and more particularly, a demand responselayer 112 thereof) to limit, cap, profit-from, or otherwise manage thebuilding or campus's energy spend. For example, using time-of-usepricing information for an upcoming hour that indicates an unusuallyhigh price per kilowatt hour, the system may use its control of aplurality of building systems to limit cost without too drasticallyimpacting occupant comfort. To make such a decision and to conduct suchactivity, the smart building manager 106 may use data such as arelatively high load forecast for a building and information that energystorage levels or distributed energy levels are low. The smart buildingmanager 106 may accordingly adjust or select a control strategy toreduce ventilation levels provided to unoccupied areas, reduce serverload, raise a cooling setpoint throughout the building, reserve storedpower for use during the expensive period of time, dim lights inoccupied areas, turn off lights in unoccupied areas, and the like.

The smart building manager 106 may provide yet other services to improvebuilding or grid performance. For example, the smart building manager106 may provide for expanded user-driven load control (allowing abuilding manager to shed loads at a high level of system/devicegranularity). The smart building manager 106 may also monitor andcontrol power switching equipment to route power to/from the mostefficient sources or destinations. The smart building manager 106 maycommunicate to the power switching equipment within the building orcampus to conduct “smart” voltage regulation. For example, in the eventof a brownout, the smart building manager 106 may prioritize branches ofa building's internal power grid—tightly regulating and ensuring voltageto high priority equipment (e.g., communications equipment, data centerequipment, cooling equipment for a clean room or chemical factory, etc.)while allowing voltage to lower priority equipment to dip or be cut offby the smart grid (e.g., the power provider). The smart building manager106 or the DR layer 112 may plan these activities or proactively beginload shedding based on grid services capacity forecasting conducted by asource on the smart grid or by a local algorithm (e.g., an algorithm ofthe demand response layer). The smart building manager 106 or the DRlayer 112 may further include control logic for purchasing energy,selling energy, or otherwise participating in a real-time or nearreal-time energy market or auction. For example, if energy is predictedto be expensive during a time when the DR layer 112 determines it canshed extra load or perhaps even enter a net-positive energy state usingenergy generated by solar arrays, or other energy sources of thebuilding or campus, the DR layer 112 may offer units of energy duringthat period for sale back to the smart grid (e.g., directly to theutility, to another purchaser, in exchange for carbon credits, etc.).

In some exemplary embodiments, the DR layer 112 may also be configuredto support a “Grid Aware” plug-in hybrid electric vehicle(PHEV)/electric vehicle charging system instead of (or in addition to)having the charging system in the vehicles be grid-aware. For example,in buildings that have vehicle charging stations (e.g., terminals in aparking lot for charging an electric or hybrid vehicle), the DR layer112 can decide when to charge the vehicles (e.g., when to enable thecharging stations, when to switch a relay providing power to thecharging stations, etc.) based upon time, real time pricing (RTP)information from the smart grid, or other pricing, demand, orcurtailment information from the smart grid. In other embodiments, eachvehicle owner could set a policy that is communicated to the chargingstation and back to the DR layer 112 via wired or wirelesscommunications that the DR layer 112 could be instructed to follow. Thepolicy information could be provided to the DR layer 112 via anenterprise application 124, a vehicle information system, or a personalportal (e.g., a web site vehicle owners are able to access to input, forexample, at what price they would like to enable charging). The DR layer112 could then activate the PHEV charging station based upon that policyunless a curtailment event is expected (or occurs) or unless the DRlayer 112 otherwise determines that charging should not occur (e.g.,decides that electrical storage should be conducted instead to help withupcoming anticipated peak demand). When such a decision is made, the DRlayer 112 may pre-charge the vehicle or suspend charge to the vehicle(e.g., via a data command to the charging station). Vehicle charging maybe restricted or turned off by the smart building manager during periodsof high energy use or expensive energy. Further, during such periods,the smart building manager 106 or the DR layer 112 may be configured tocause energy to be drawn from plugged-in connected vehicles tosupplement or to provide back-up power to grid energy.

Using the real time (or near real-time) detailed information regardingenergy use in the building, the smart building manager 106 may maintaina greenhouse gas inventory, forecast renewable energy use, surpluses,deficits, and generation, and facilitate emission allocation, emissiontrading, and the like. Due to the detailed and real-time or nearreal-time nature of such calculations, the smart building manager 106may include or be coupled to a micro-transaction emission tradingplatform.

The DR layer 112 may further be configured to facilitate the storage ofon-site electrical or thermal storage and to controllably shiftelectrical loads from peak to off peak times using the stored electricalor thermal storage. The DR layer 112 may be configured to significantlyshed loads during peak hours if, for example, high price or contractedcurtailment signals are received, using the stored electrical or thermalstorage and without significantly affecting building operation orcomfort. The integrated control layer 116 may be configured to use abuilding pre-cooling algorithm in the night or morning and rely oncalculated thermal storage characteristics for the building in order toreduce peak demand for cooling. Further, the integrated control layer116 may be configured to use inputs such as utility rates, type ofcooling equipment, occupancy schedule, building construction, climateconditions, upcoming weather events, and the like to make controldecisions (e.g., the extent to which to pre-cool, etc.).

Automated Measurement & Verification Layer

FIGS. 1A and 1B are further shown to include an automated measurementand validation layer 110 configured to evaluate building system (andsubsystem) performance. The automated measurement and validation (AM&V)layer 110 may implement various methods or standards of theinternational performance measurement and validation (IPMVP) protocol.In an exemplary embodiment, the AM&V layer 110 is configured toautomatically (e.g., using data aggregated by the AM&V layer 110,integrated control layer 116, building subsystem integration layer 118,FDD layer 114, or otherwise) verify the impact of the integrated controllayer 116, the FDD layer 114, the DR layer 112, or other energy-savingstrategies of the smart building manager 106. For example, the AM&Vlayer 110 may be used to validate energy savings obtained by capitalintensive retrofit projects that are monitored or managed post retrofitby the smart building manager. The AM&V layer 110 may be configured tocalculate, for example, a return on investment date, the money savedusing pricing information available from utilities, and the like. TheAM&V layer 110 may allow for user selection of the validation method(s)it uses. For example, the AM&V layer 110 may allow for the user toselect IPMVP Option C which specifies a method for the direct comparisonof monthly or daily energy use from a baseline model to actual data fromthe post-installation measurement period. IPMVP Option C, for example,may specify for adjustments to be made of the base-year energy modelanalysis to account for current year over base year changes inenergy-governing factors such as weather, metering period, occupancy, orproduction volumes. The AM&V layer 110 may be configured to track (e.g.,using received communications) the inputs for use by such a validationmethod at regular intervals and may be configured to make adjustments toan “adjusted baseline energy use” model against which to measuresavings. The AM&V layer 110 may further allow for manual or automaticnon-routine adjustments of factors such as changes to the facility size,building envelope, or major equipment. Algorithms according to IPMVPOption B or Option A may also or alternatively be used or included withthe AM&V layer 110. IPMVP Option B and IPMVP Option A involve measuringor calculating energy use of a system in isolation before and after itis retrofitted. Using the building subsystem integration layer (or otherlayers of the BMS), relevant data may be stored and the AM&V layer 110may be configured to track the parameters specified by IPMVP Option B orA for the computation of energy savings for a system in isolation (e.g.,flow rates, temperatures, power for a chiller, etc.).

The AM&V layer 110 may further be configured to verify that controlstrategies commanded by, for example, the integrated control layer orthe DR layer are working properly. Further, the AM&V layer 110 may beconfigured to verify that a building has fulfilled curtailment contractobligations. The AM&V layer 110 may further be configured as anindependent verification source for the energy supply company (utility).One concern of the utility is that a conventional smart meter may becompromised to report less energy (or energy consumed at the wrongtime). The AM&V layer 110 can be used to audit smart meter data (orother data used by the utility) by measuring energy consumption directlyfrom the building subsystems or knowledge of building subsystem usageand comparing the measurement or knowledge to the metered consumptiondata. If there is a discrepancy, the AM&V layer may be configured toreport the discrepancy directly to the utility. Because the AM&V layermay be continuously operational and automated (e.g., not based on amonthly or quarterly calculation), the AM&V layer may be configured toprovide verification of impact (e.g., of demand signals) on a granularscale (e.g., hourly, daily, weekly, etc.). For example, the AM&V layermay be configured to support the validation of very short curtailmentcontracts (e.g., drop X kW/h over 20 minutes starting at 2:00 pm) actedupon by the DR layer 112. The DR layer 112 may track meter data tocreate a subhourly baseline model against which to measure loadreductions. The model may be based on average load during a period ofhours prior to the curtailment event, during the five prior uncontrolleddays, or as specified by other contract requirements from a utility orcurtailment service provider (e.g., broker). The calculations made bythe AM&V layer 110 may be based on building system energy models and maybe driven by a combination of stipulated and measured input parametersto estimate, calculate, apportion, and/or plan for load reductionsresulting from the DR control activities.

The AM&V layer 110 may yet further be configured to calculate energysavings and peak demand reductions in accordance with standards,protocols, or best practices for enterprise accounting and reporting ongreenhouse gas (GHG) emissions. An application may access data providedor calculated by the AM&V layer 110 to provide for web-based graphicaluser interfaces or reports. The data underlying the GUIs or reports maybe checked by the AM&V layer 110 according to, for example, the GHGProtocol Corporate Accounting Standard and the GHG Protocol for ProjectAccounting. The AM&V layer 110 preferably consolidates data from all thepotential sources of GHG emissions at a building or campus andcalculates carbon credits, energy savings in dollars (or any othercurrency or unit of measure), makes adjustments to the calculations oroutputs based on any numbers of standards or methods, and createsdetailed accountings or inventories of GHG emissions or emissionreductions for each building. Such calculations and outputs may allowthe AM&V layer 110 to communicate with electronic trading platforms,contract partners, or other third parties in real time or near real timeto facilitate, for example, carbon offset trading and the like.

The AM&V Layer 110 may be further configured to become a “smart electricmeter” a or substitute for conventional electric meters. One reason theadoption rate of the “Smart Electric Grid” has conventionally been lowis that currently installed electric meters must be replaced so that themeters will support Real Time Pricing (RTP) of energy and other datacommunications features. The AM&V layer 110 can collect interval-basedelectric meter data and store the data within the system. The AM&V layer110 can also communicate with the utility to retrieve or otherwisereceive Real Time Pricing (RTP) signals or other pricing information andassociate the prices with the meter data. The utility can query thisinformation from the smart building manager (e.g., the AM&V layer 110,the DR layer 112) at the end of a billing period and charge the customerusing a RTP tariff or another mechanism. In this manner, the AM&V layer110 can be used as a “Smart Electric Meter”.

When the AM&V layer 110 is used in conjunction with the DR layer 112,building subsystem integration layer 118, and enterprise integrationlayer 108, the smart building manager 106 can be configured as an energyservice portal (ESP). As an ESP, the smart building manager 106 maycommunicably or functionally connect the smart grid (e.g., energy supplycompany, utility, ISO, broker, etc.) network to the metering and energymanagement devices in a building (e.g., devices built into appliancessuch as dishwashers or other “smart” appliances). In other words, thesmart building manager 106 may be configured to route messages to andfrom other data-aware (e.g., Real Time Pricing (RTP) aware, curtailmentsignal aware, pricing aware, etc.) devices and the energy supplycompany. In this configuration, building subsystems that are not RTPaware will be managed by the DR layer 112 while devices that are RTPaware can get signals directly from the utility. For example, if avehicle (e.g., PHEV) is programmed to charge only when the price ofelectricity is below $0.1/kWh, the PHEV can query the utility throughthe smart building manager and charge independently from the DR layer112.

In an exemplary embodiment, the AM&V layer described in U.S. ProvisionalApplication No. 61/302,854, filed Feb. 9, 2010 can be used as AM&V layer110 or a part thereof.

Enterprise Integration Layer

The enterprise integration layer 108 shown in FIG. 1A or FIG. 1B isconfigured to serve clients or local applications with information andservices to support a variety of enterprise-level applications. Theenterprise integration layer 108 may be configured to communicate (inreal time or near real time) with the smart grid 104 and energyproviders and purchasers 102. More particularly, in some embodiments theenterprise integration layer 108 may communicate with “smart meters,”automated meter interfaces with utilities, carbon emission tracking andaccounting systems, energy reporting systems, a building occupantinterface, and traditional enterprise productivity applications (e.g.,maintenance management systems, financial systems, workplace and supplychain management systems, etc.). The enterprise integration layer 108may be configured to use protocols and methods as described above withrespect to other layers or otherwise.

Communication and Security Features

Referring again to FIG. 3, the smart building manager may be configuredto provide drivers for BACnet, LON, N2, Modbus, OPC, OBIX, MIG, SMTP,XML, Web services, and various other wireless communications protocolsincluding Zigbee. These drivers may be implemented within or used by theservice bus adapters or subsystem adapters. The service bus for thesmart building manager may be configured to communicate using any numberof smart grid communications standards. Such standards may be utilizedfor intra-manager communication as well as communication with a smartgrid component (e.g., utility company, smart meter, etc.). For example,the smart building manager may be configured to use the ANSIC12.22/C12.19 protocol for some internal communications (e.g., DRevents) as well as for communications with the smart grid. The servicebus adapters and subsystem adapters convert received messages into anormalized messaging format for use on the service bus. In an exemplaryembodiment the service bus is flexible, making use of IT-centric messagequeuing technologies (e.g., Open AMQ, MSMQ, and WebSphere MQ) to assurereliability, security, scalability, and performance. Service busadapters enable layers and applications to communicate among one anotherand/or to the various in-building or external systems (e.g., viasubsystem adapters). Stored communications rules may be used by theservice bus adapters, subsystem adapters, or other components of thesystem to catch or correct communications failures. Communications andaction-failure rules may also be configured for use by the action layersof the system. For example, the DR layer can check for whether an actionrequested or commanded by the DR layer has completed. If not, the DRlayer can take a different action or a corrective action (e.g., turn offan alternate load, adjust additional setpoints, trigger a focused FDDactivity, etc.) to ensure that DR needs are met. The smart buildingmanager can also determine if someone has provided a DR override commandto the system and take corrective action if available. If correctiveaction is unavailable, an appropriate message or warning may be sent toa DR partner (e.g., a utility co., an energy purchaser via the smartgrid, etc.).

The smart building manager 106 may reside on (e.g., be connected to) anIP Ethernet network utilizing standard network infrastructure protocolsand applications (e.g., DNS, DHCP, SNTP, SNMP, Active Directory, etc.)and can also be secured using IT security best practices for thosestandard network infrastructure protocols and applications. For example,in some embodiments the smart building manager may include or beinstalled “behind” infrastructure software or hardware such as firewallsor switches. Further, configurations in the smart building manager 106can be used by the system to adjust the level of security of the smartbuilding manager 106. For example, the smart building manager 106 (orparticular components thereof) can be configured to allow its middlelayers or other components to communicate only with each other, tocommunicate with a LAN, WAN, or Internet, to communicate with selectdevices having a building service, or to restrict communications withany of the above mentioned layers, components, data sources, networks,or devices. The smart building manager 106 may be configured to supporta tiered network architecture approach to communications which mayprovide for some measure of security. Outward facing components areplaced in a less secure “tier” of the network to act as a point of entryto/from the smart building manager 106. These outward facing componentsare minimized (e.g., a web server receives and handles all requests fromclient applications) which limits the number of ways the system can beaccessed and provides an indirect communications route between externaldevices, applications, and networks and the internal layers or modulesof the smart building manager 106. For example, “behind” the outwardfacing “first tier” may lie a more secure tier of the network thatrequires authentication and authorization to occur at the first tierbefore functions of the more secure tier are accessed. The smartbuilding manager 106 may be configured to include firewalls between suchtiers or to define such tiers to protect databases or core components ofthe system from direct unauthorized access from outside networks.

In addition to including or implementing “infrastructure” type securitymeasures as the type disclosed above, the smart building manager may beconfigured to include a communications security module configured toprovide network message security between the smart building manager andan outside device or application. For example, if SOAP messaging overHTTP is used for communication at the enterprise integration layer, theSOAP messages may be concatenated to include an RC2 encrypted headercontaining authentication credentials. The authentication credentialsmay be checked by the receiving device (e.g., the smart buildingmanager, the end application or device, etc.). In some embodiments theencrypted header may also contain information (e.g., bits) configured toidentify whether the message was tampered with during transmission, hasbeen spoofed, or is being “replayed” by an attacker. If a message doesnot conform to an expected format, or if any part of the authenticationfails, the smart building manager may be configured to reject themessage and any other unauthorized commands to the system. In someembodiments that use HTTP messages between the application and the smartbuilding manager, the smart building manager may be configured toprovide SSL for message content security (encryption) and/or Formsauthentication for message authentication.

The smart building manager 106 may yet further include an accesssecurity module that requires any application to be authenticated withuser credentials prior to logging into the system. The access securitymodule may be configured to complete a secure authentication challenge,accomplished via a public or private key exchange (e.g., RSA keys) of asession key (e.g., an RC2 key), after a login with user credentials. Thesession key is used to encrypt the user credentials for theauthentication challenge. After the authentication challenge, thesession key is used to encrypt the security header of the messages. Onceauthenticated, user actions within the system are restricted byaction-based authorizations and can be limited. For example, a user maybe able to command and control HVAC points, but may not be able tocommand and control Fire and Security points. Furthermore, actions of auser within the smart building manager are written to memory via anaudit trail engine, providing a record of the actions that were taken.The database component of the smart building manager 106 (e.g., forstoring device information, DR profiles, configuration data, pricinginformation, or other data mentioned herein or otherwise) can beaccessible via an SQL server that is a part of the building managementserver or located remotely from the smart building manager 106. Forexample, the database server component of the smart building manager 106may be physically separated from other smart building manager componentsand located in a more secure tier of the network (e.g., behind anotherfirewall). The smart building manager 106 may use SQL authentication forsecure access to one or more of the aforementioned databases.Furthermore, in an exemplary embodiment the smart building manager canbe configured to support the use of non-default instances of SQL and anon-default TCP port for SQL. The operating system of the smart buildingmanager may be a Windows-based operating system.

Each smart building manager 106 may provide its own security and is notreliant on a central server to provide the security. Further, the samerobustness of the smart building manager 106 that provides the abilityto incorporate new building subsystem communications standards, modules,drivers and the like also allows it to incorporate new and changingsecurity standards (e.g., for each module, at a higher level, etc.).

Multi-Campus/Multi-Building Energy Management

The smart building manager 106 shown in the Figures may be configured tosupport multi-campus or multi-building energy management services. Eachof a plurality of campuses can include a smart building managerconfigured to manage the building, IT, and energy resources of eachcampus. In such an example, the building subsystems shown, e.g, in FIGS.1A and 1B may be a collection of building subsystems for multiplebuildings in a campus. The smart building manager may be configured tobi-directionally communicate with on-site power generation systems(e.g., distributed power sources, related services, solar arrays, fuelcell arrays, diesel generators, combined heat and power (CHP) systems,etc.), plug-in hybrid electric vehicle (PHEV) systems, and energystorage systems (e.g., stationary energy storage, thermal energystorage, etc.). Data inputs from such sources may be used by the demandand response layer of the smart building manager to make demand orresponse decisions and to provide other ancillary services to aconnected smart grid (e.g., utility, smart meter connected to a utility,etc.) in real time or near real time. For example, the smart buildingmanager may communicate with smart meters associated with an energyutility and directly or indirectly with independent systems operators(ISOs) which may be regional power providers. Using thesecommunications, and its inputs from devices of the campus, the smartbuilding manager (e.g., the demand response layer) is configured toengage in “peak shaving,” “load shedding,” or “load balancing” programswhich provide financial incentives for reducing power draw duringcertain days or times of day. The demand response layer or other controlalgorithms of the smart building manager (e.g., control algorithms ofthe integrated control layer) may be configured to use weather forecastinformation to make setpoint or load shedding decisions (e.g., so thatcomfort of buildings in the campus is not compromised). The smartbuilding manager may be configured to use energy pricing information,campus energy use information, or other information to optimize businesstransactions (e.g., the purchase of energy from the smart grid, the saleof energy to the smart grid, the purchase or sale of carbon credits withenergy providers and purchasers, etc.). The smart building manager isconfigured to use the decisions and processing of the demand responselayer to affect control algorithms of the integrated control layer.

While FIG. 1B is shown as a tightly-coupled smart building manager 106,in some embodiments the processing circuit of FIG. 1B (including thelayers/modules thereof) may be distributed to different servers thattogether form the smart building manager having the control featuresdescribed herein. In embodiments where the smart building manager 106 iscontrolling an entire campus or set of campuses, one or more smartbuilding managers may be layered to effect hierarchical controlactivities. For example, an enterprise level smart building manager mayprovide overall DR strategy decisions to a plurality of lower levelsmart building managers that process the strategy decisions (e.g., usingthe framework shown in FIG. 3) to effect change at an individual campusor building. By way of further example, the “integrated control layer”116 and the “building system integration layer” 118 may be replicatedfor each building and stored within lower level smart building serverswhile a single enterprise level smart building manager may provide asingle higher level layer such the DR layer. Such a DR layer can executea campus-wide DR strategy by passing appropriate DR events to theseparate lower level smart building mangers having integrated controllayers and building system integration layers. Higher level servers mayprovide software interfaces (APIs) to the one or more lower levelservers so that the one or more lower level servers can requestinformation from the higher level server, provide commands to the higherlevel server, or otherwise communicate with the layers or data of thehigher level server. The reverse is also true, APIs or other softwareinterfaces of the lower level servers may be exposed for consumption bythe higher level server. The software interfaces may be web servicesinterfaces, relational database connections, or otherwise.

Statistical Process Control and Fault Detection Using Moving Averages

A moving average can be used as an input to a statistical processcontrol strategy for detecting a variation in the behavior of thebuilding management system. In general, moving averages are a class ofstatistical metrics that utilize previously calculated averages in theircomputation. Moving averages may advantageously reduce processing timesand memory requirements relative to other statistical processingstrategies, since only a subset of the data values needs to be retained.For example, a standard average may be calculated using the formula:

${avg}_{i} = \frac{\sum\limits_{i = 1}^{n}x_{i}}{i}$

where i is the number of data points and x_(i) is the i^(th) data point.A standard average requires summing the data points each time a new datapoint is collected and requires retaining each data point in memory. Amoving average, by contrast, can use the previously calculated averageto generate a new average when x_(i+1) becomes available. For example, amoving average may be calculated using:

${mov\_ avg}_{i + 1} = \frac{x_{i + 1} + {i*{avg}_{i}}}{i + 1}$

where x_(i+1) is the most recent data point and avg_(i) is thepreviously computed average.

Weighted moving averages are a subclass of moving averages that applyweightings to the various subsets of data. For example, a weightedmoving average may weight more recent data values higher than oldervalues. In this way, the weighted moving average provides a currentmetric on the underlying data. Exponentially weighted averages (EWMAs)have been used to diagnose faults in building management controllers.See, U.S. Pat. No. 5,682,329 to Seem et al. EWMAs utilize exponentialweightings that can be used to give greater emphasis to more recentvalues. A variety of equations exist for calculating an EWMA. Forexample, an EWMA may be calculated according to the following function:

${\overset{\_}{x}}_{t} = {\sum\limits_{j = 0}^{\infty}{{\lambda ( {1 - \lambda} )}^{j}x_{t - j}}}$

where x _(t) is the EWMA at time t; λ is an exponential smoothingconstant or filter value; and x_(t-j) is the value of the signal at timet−j.

Embodiments of the present application can include using EWMA-basedcontrol strategies to detect errors. In one example relating to an HVACsystem, a building management system controller may sample the positionof a damper that it controls. The controller can then calculate the EWMAof the position value. If the EWMA exceeds a threshold, the controllermay determine that the damper is in a saturation condition. Thecontroller can then notify a user of the potential fault.

In another example, a network of controllers may collect EWMA values fora temperature error. A design criterion for the network may be set suchthat ninety five percent of all controllers should have a temperatureerror EWMA of below 2° F. An EWMA of the temperature error greater than2° F. could be used to estimate or predict system faults while an EWMAof less than 2° F. could indicate that the network is working properly.

A statistical process control strategy of varying exemplary embodimentsmay detect variations in measured data by evaluating the measured datarelative to a trained statistical model (e.g., a statistical processcontrol chart). The trained statistical model may be based onmeasurements taken during a training period (e.g., while the buildingmanagement system is operating normally, during a steady state operatingperiod, etc.). The trained statistical model is used to predict behaviorfor the building management system under normal operating conditions.Measured data that falls outside of the parameters of the statisticalmodel may be considered to be statistically significant and indicatethat the predicted behavior is no longer valid and/or that faults existin the building management system.

Referring now to FIG. 5A, a flow diagram of a process 500 for usingstatistical process control to detect faults in a building managementsystem is shown, according to an exemplary embodiment. Process 500includes collecting training data from the building management system(step 502). During the training period, training data (i.e. performancevalues) are collected to build a history of performance values. Forexample, the performance values may be measured temperature values,calculated error rates, moving averages, measured power consumptions, orany other historical performance data. The history of performance valuesis used to determine if the BMS is operating normally.

Once a sufficient history of performance values has been built, thehistory can be used to generate a statistical model (step 504).Generally speaking, the statistical model is a set of metrics based on,calculated using, or describing the history of performance values. Thestatistical model is used to predict a behavior of the BMS.

Process 500 is further shown to include calculating an EWMA using newperformance values (step 506). The new performance values are collectedafter the training period. In some embodiments, the new performancevalues are collected by building management controllers and sent via anetwork to a remote location for calculation of the EWMA. In otherembodiments, the EWMA is calculated directly on a local BMS controllerthat collects the performance values.

Process 500 is yet further shown to include comparing the EWMAcalculated in step 506 to the statistical model generated in step 504(step 508). For example, the EWMA calculated in step 506 can be comparedto the statistical model generated in step 504 to test for statisticalsignificance, i.e. if the EWMA is an outlier in relation to thestatistical model. If a specified number of outliers are detected, thesystem can determine that the newly observed behavior of the system nolonger matches the predicted behavior (i.e., described by thestatistical model of step 504) and that appropriate action is necessary.If the new performance value is determined to be statisticallysignificant in step 508, i.e. it is an outlier in relation to thestatistical model of behavior for that performance value, any number ofactions may be taken by the BMS and/or a user. For example, if process500 is used within fault detection and diagnostics (FDD) layer 114,statistical significance of new performance values may indicate that afault condition exists. FDD layer 114 may then notify a user, amaintenance scheduling system, or a control algorithm configured toattempt to further diagnose the fault, to repair the fault, or towork-around the fault. If process 500 is used in automated measurementand validation (AM&V) layer 110, a statistical significance of newperformance values may indicate that a predicted model of a powerconsumption is no longer valid. AM&V layer 110 may then attempt toupdate the statistical model to better predict future power consumptionsand/or notify FDD layer 114 that a fault condition may exist.

Referring now to FIG. 5B, a detailed diagram of a fault detection moduleis shown, according to an exemplary embodiment. Automated faultdetection module 412 includes EWMA generator 520, which receivesbuilding management system data such as meter data 402, weather data 404and building subsystem data 408. EWMA generator 520 calculatesexponentially weighted moving averages of the data or a value calculatedusing the data, and outputs them as performance indices 410. Performanceindices 410 may be stored in performance value database 524.

The EWMAs may be calculated directly on building equipment controllersnot shown in FIG. 5B and transmitted to automated fault detection module412 (e.g., via a network, via communications electronics, etc.). Inother embodiments, some EWMAs are calculated directly on the buildingequipment controllers, while others are calculated remotely by EWMAgenerator 520.

EWMA generator 520 may calculate the moving averages by first removingsudden spikes in the data by applying an anti-spike filter or an outlierfilter. For example, EWMA generator 520 may use a generalized extremestudentized distribution method to remove outliers in the buildingmanagement system data. EWMA generator 520 may also sub-sample thebuilding management system data to reduce the effects of autocorrelationin the data. For example, a sampling interval greater than or equal tothe time constant of the process being controlled by the buildingequipment controller may be used.

Automated fault detection module 412 includes performance value database524. Performance value database 524 can store a history of performancevalues used by training component 522 to generate statistical models,such as model data 406. In one embodiment, the history of performancevalues stored in performance value database 524 contains a record ofEWMAs previously calculated by EWMA generator 520. In anotherembodiment, performance value database 524 contains a history of rawdata values from the building management system.

Automated fault detection module 412 is further shown to includetraining component 522 which performs statistical operations on thehistory of performance values to produce one or more thresholdparameters as inputs to threshold evaluator 526. The thresholdparameters are statistical predictors of the behavior of the buildingmanagement system, i.e. markers that define a range of normal behaviorwithin a specific statistical confidence. For example, trainingcomponent 522 may generate threshold parameters that define a modelwherein 99.7% of values observed during the training period fall withinupper and lower temperature threshold parameters.

Automated fault detection module 412 is yet further shown to includethreshold parameter evaluator 526. Threshold parameter evaluator 526 canuse the one or more threshold parameters from training component 522 todetermine if new performance values are statistically significant. Forexample, threshold parameter evaluator 526 may compare a new EWMA fromEWMA generator 520 to a trained threshold parameter to determine if thenew EWMA is statistically significant, i.e. the new EWMA falls outsideof the predicted behavior. If a new performance value is statisticallysignificant, threshold parameter evaluator 526 may notify automateddiagnostics module 414, manual diagnostics module 416, and/or GUIservices that a possible fault condition exists. Additionally, a usermay be notified that a fault may exist by GUI services causing agraphical user interface to be displayed on an electronic displaydevice. The generated graphical user interface can include an indicatorthat a fault has occurred and information regarding the estimated fault.Threshold parameter evaluator 526 may notify automated diagnosticsmodule 414, manual diagnostics module 416, or GUI services only if aplurality of statistically significant performance values are detected.

Statistical Process Control Chart Generation

In many of the varying exemplary embodiments, the statistical model usedin the statistical process control strategy may be a statistical processcontrol chart (e.g., an EWMA control chart, etc.). Such charts typicallyutilize upper and lower control limits relative to a center line todefine the statistical boundaries for the process. New data values thatare outside of these boundaries indicate a deviation in the behavior ofthe process. In some cases, the charts may also contain one or morealarm thresholds that define separate alarm regions below the uppercontrol limit and above the lower control limits. A processor utilizingsuch a chart may determine that a new data value is within orapproaching an alarm region and generate an alert, initiate a diagnosticroutine, or perform another action to move the new data values away fromthe alarm regions and back towards the center line. Although thisdisclosure variously mentions the term “chart,” many of the exemplaryembodiments of the disclosure will operate without storing or displayinga graphical representation of a chart. In such embodiments, aninformation structure suitable for representing the data of astatistical process control chart may be created, maintained, updated,processed, and/or stored in memory. Description in this disclosure thatrelates to systems having statistical process control charts orprocesses acting on or with statistical process control charts isintended to encompass systems and methods that include or act on suchsuitable information structures.

Referring now to FIG. 6A, a flow diagram of a process for generating astatistical process control chart is shown, according to an exemplaryembodiment. Process 600 includes receiving a history of performancevalues (step 602). The performance values may be data collected by theBMS during normal operations. For example, the performance values may bemeasured temperature values, calculated error rates, measured powerconsumptions, or any other data that can be used to determine whetherthe BMS is operating normally. In another embodiment, the performancevalues are exponentially weighted moving averages of data from the BMS.A history of 150-500 performance values may be sufficient to create thestatistical model, although more or less may be used in varyingexemplary embodiments.

Process 600 is also shown to include generating a target parameter (step604). The target parameter provides a target metric for the system undernormal operating conditions. In one embodiment, the target parameter isthe statistical mean of the history of performance values from step 602,i.e. the simple average of the performance values. In anotherembodiment, the median of the history is used. In yet anotherembodiment, a moving average of the performance values can be used. Forexample, the history of performance values may be measured temperaturevalues that range from 95° F. to 105° F., with a simple average of 100°F. Therefore, a target parameter of 100° F. may be used to predictfuture temperatures for a normally operating BMS. Future performancevalues that vary greatly from the target parameter may indicate a faultin the BMS, a change in behavior of the BMS, or that the statisticalmodel needs to be updated.

Process 600 is further shown to include generating an estimator of scale(step 606). Estimators of scale generally provide a metric thatdescribes how spread out a set of performance values is relative to thetarget parameter. In one embodiment, the standard deviation of thehistory of performance values is calculated using the target parameterfrom step 604 and the performance values from step 602. For example, thehistory of performance values may contain measured temperatures thatrange from 95° F. to 105° F., with a simple average of 100° F. Assumingthat the performance values are distributed normally, i.e. they conformto a Gaussian distribution, a calculated standard deviation of 1.5° F.indicates that approximately 99.7% of the measured temperatures fallwithin the range of 95.5° F. to 104.5° F. However, a non-normaldistribution of the performance values or the presence of outlierperformance values can affect the ability of a standard deviation togauge the spread of the data.

In a preferred embodiment, a robust estimator of scale is calculated instep 606. Robust estimators of scale differ from standard estimators ofscale, such as a standard deviation, by reducing the effects of outlyingperformance values. A variety of different types of robust estimators ofscale may be used in conjunction with the present invention. Forexample, a robust estimator of scale that uses a pairwise differenceapproach may be used. Such approaches typically have a higher Gaussianefficiency than other robust approaches. These approaches provide auseful metric on the interpoint distances between elements of two arraysand can be used to compare a predicted behavior and an observed behaviorin the building management system. For example, one robust estimator ofscale may be defined as:S_(n)=c_(n)*1.1926*med_(i){med_(j)(|x_(i)−x_(j)|)} where the set ofmedians for j=1, . . . , n is first calculated as an inner operation.Next, the median of these results is calculated with respect to the ivalues. The median result is then multiplied by 1.1926, to provideconsistency at normal distributions. A correction factor c_(n) may alsobe applied and is typically defined as 1 if n is even. If n is odd,c_(n) can be calculated as:

$c_{n} = {\frac{n}{n - 0.9}.}$

The described S_(n) estimator of scale has a Gaussian efficiency ofapproximately 58%. Computational techniques are also known that computeS_(n) in O(n log n) time.

In another exemplary embodiment, Q_(n) may be used as a robust estimatorof scale, where Q_(n) is defined as Q_(n)=d_(n)*2.2219*1^(st)quartile(|x_(i)−x_(j)|:i<j). As with S_(n), a pairwise differenceapproach is taken to compute Q_(n). If n is even, correction factord_(n) can be defined as:

$d_{n} = \frac{n}{n + 1.4}$

and if n is odd, correction factor d_(n) can be defined as:

$d_{n} = {\frac{n}{n + 3.8}.}$

The Q_(n) estimator of scale provides approximately an 82% Gaussianefficiency and can also be computed in O(n log n) time.

Process 600 is yet further shown to include generating a thresholdparameter (step 608). In some embodiments, the threshold may be based onthe estimator of scale from step 606. For example, the thresholdparameters may be calculated using: threshold=μ±K*σ where K is aconstant, μ is the target parameter and σ is the estimator of scale.

A threshold parameter can be compared against a new performance valuefrom the BMS to determine whether the new performance value isstatistical significant. For example, if the history of performancevalues are measured temperatures that range from 95° F. to 105° F. witha simple average of 100° F. and a standard deviation of 1.5° F., K maybe set to 3 to provide an upper threshold parameter of 104.5° F.Assuming that the data values are normally distributed, this means thatapproximately 99.85% of the historical temperatures fall below thisthreshold parameter. New temperature measurements that are equal to orgreater than the threshold parameter may be statistically significantand indicate that a fault condition exists.

Referring now to FIG. 6B, a more detailed flow diagram of a process forgenerating a statistical process control chart is shown, according to anexemplary embodiment. Process 620 includes receiving a history ofperformance values from the BMS (step 622). In one embodiment, thehistory of performance values is built during a training period wherethe BMS is operating normally.

Process 620 is also shown to include checking the history of performancevalues for autocorrelation (step 624). In general, autocorrelationmeasures how closely a newer set of performance values follows thepattern of previous performance values. Any known method of testingautocorrelation may be used in step 624. For example, a lag-onecorrelation coefficient may be calculated to test for autocorrelation.If the coefficient is high, the data is assumed to be autocorrelated. Ifthe coefficient is low, the data is assumed not to be autocorrelated.

Process 620 optionally includes checking the history of performancevalues for normality, i.e how closely the history conforms to a Gaussiandistribution (step 626). Any suitable method of testing for normalitymay be used in step 626. For example, a Lillifors test may be used totest the null hypothesis that the data is normal against the alternativehypothesis that the data is not normal.

Process 620 is further shown to include generating a target parameterusing the history of performance values (step 628). The characteristicsof the history tested in steps 624 and 626 can be used to determine howthe target parameter is generated. Any suitable metrics that reduce theeffects of autocorrelation and non-normal data may be used. For example,if the history is determined not to be autocorrelated in step 624, themedian of the history may be used as the target parameter. In otherexamples, the EWMA or the simple mean of the history is used.

If the data is determined to be autocorrelated in step 624, anautoregressive model may be used to fit the data and the residuals usedto calculate the target parameter. For example, an AR(1) model may befit to the history using the equation: x_(t)=φ₀+φ₁*x_(t-1)+e_(t) where xis a predicted value, x_(t-1) is a previous value, φ₀ and φ₁ areconstants and e_(t) is a residual. The target parameter can then becalculated using the residual values. For example, the target parametercan be the simple mean of the residual values. In other embodiments, thetarget parameter can be the median of the residual values, a movingaverage of the residual values, or any other statistical metric thatgenerally corresponds to the center of the distribution of residualvalues.

Process 620 is yet further shown to include generating an estimator ofscale (step 630). The estimator of scale may be generated using thetarget parameter and/or the history of performance values. The type ofestimator of scale that is used may be determined based on the resultsof steps 624, 626 and/or the type of target parameter used in step 528.If the target parameter of step 628 is the median of the history and thehistory is not autocorrelated, a robust estimator of scale may be foundfor the history itself. However, if the data is autocorrelated and thetarget parameter is determined using an autoregressive model, a robustestimator of scale may calculated using the residuals of theautoregressive model. In other embodiments, other types of estimators ofscale are used, such as a standard deviation.

Process 620 is also shown to include generating a threshold parameter(step 632). The threshold parameter may be calculated using theestimator of scale of step 626 and the target parameter of step 628. Insome embodiments, the threshold parameter is calculated by multiplyingthe estimator of scale by a constant and adding or subtracting thatvalue from the target parameter, as in step 606 of method 600. Forexample, if the estimator of scale is a simple standard deviation, theconstant may be set to 3 to generate upper and lower thresholds thatencompass approximately 99.7% of the history. In this way, the choice ofa constant value may be used to define any number of thresholdparameters. In one embodiment, the threshold parameter is calculatedautomatically by the BMS. In another embodiment, a user may input adesired threshold parameter using a display device configured to receiveuser input. In yet another embodiment, a hybrid approach may be takenwhere the BMS automatically calculates the threshold parameter andprovided it to a display device for user confirmation of the thresholdparameter or input of a different threshold parameter.

The target parameter and one or more threshold parameters generated inprocess 600 or process 620 may also be used to generate a SPC controlchart. In such a case, the target parameter may be used as the centerline of the chart. The threshold parameters may also be used as upperand lower control limits for the SPC control chart. New data that fallsoutside of the control limits of the chart may indicate a deviation inthe behavior of the associated process.

Referring now to FIG. 7, a detailed diagram of training module 522 isshown, according to an exemplary embodiment. Training module 522 isshown to include performance value aggregator 702 which generates andmaintains a history of performance values. During training, performancevalue aggregator 702 stores performance values from the BMS asperformance indices 410 or in performance value database 524.Performance value aggregator 702 may also be configured to receive aninput from automated diagnostics module 414, manual diagnostics module416 or GUI services 422 that indicates that the system needs to beretrained. If retraining is needed, performance value aggregator 702 canupdate and store new performance values during the new training period.During a training period, performance value aggregator 702 can overwritesome data or delete some data (e.g., old data, faulty data, etc.) fromthe its performance value calculation. Once a sufficient number ofperformance values are collected, the training period ends andperformance value aggregator retrieves a history of performance valuesfrom performance indices 410 or performance value database 524.

Training module 522 includes autocorrelation evaluator 704.Autocorrelation evaluator 704 detects autocorrelation in the history ofperformance values retrieved by performance value aggregator 702. Forexample, autocorrelation evaluator 704 may use a lag-one correlationcoefficient method to test for autocorrelation in the history. Theresults of this determination are then provided to target parametergenerator 708, and may be used to determine the method to be used ingenerating the target parameter.

Training module 522 includes normality evaluator 706. Normalityevaluator determines how closely the history of performance valuesconforms to a Gaussian distribution, i.e. a bell-curve. Normal dataprovides a greater statistical confidence in the model's ability topredict behavior, although non-normal data can still be used to detectvariations in the system's behavior.

Training module 522 is further shown to include target parametergenerator 708 which uses the history of performance values fromperformance value aggregator 702 and the outputs of autocorrelationevaluator 704 and normality evaluator 706 to generate a targetparameter. The target parameter provides a statistical center for thestatistical model based on the history of performance values. Forexample, target parameter generator 708 may calculate the median of thehistory of performance values as the target parameter. Once targetparameter generator 708 generates a target parameter, an estimator ofscale is calculated by estimator of scale generator 710. Estimator ofscale generator 708 uses the output of target parameter generator 708and the history of performance values from performance value aggregator702 to generate an estimator of scale for the history, i.e. a metric onhow spread out the distribution of data is. In one embodiment, a robustestimator of scale is calculated by estimator of scale generator 710.

Training module 522 yet further includes threshold parameter generator712, which uses the outputs of target parameter generator 708 andestimator of scale generator 710 to generate one or more thresholdparameters. The one or more threshold parameters are then provided tothreshold parameter evaluator 712 for comparison against new performancevalues.

Statistical Process Control to Measure and Verify Energy Savings

Referring now to FIG. 8, a process 800 for measuring and verifyingenergy savings in a building management system is shown, according to anexemplary embodiment. Process 800 may be used by automated measurementand verification layer 110 to measure and verify energy savings in thebuilding management system. Process 800 is shown to include retrievinghistorical building and building environment data from a pre-retrofitperiod (step 802). Input variables retrieved in step 802 and used insubsequent steps may include both controllable variables (e.g.,variables that may be controlled by a user such as occupancy of an area,space usage, occupancy hours, etc.) and uncontrollable variables (e.g.,outdoor temperature, solar intensity and duration, other weatheroccurrences, degree days, etc.). Variables which are not needed (i.e.,they do not have an impact on the energy savings calculations) may bediscarded or ignored by automated measurement and verification layer110.

Process 800 is also shown to include using historical data to create abaseline model that allows energy usage (e.g., kWh) or power consumption(e.g., kW) to be predicted from varying input or predictor variables(e.g., occupancy, space usage, occupancy hours, outdoor air temperature,solar intensity, degree days, etc.) (step 804). For example, powerconsumptions measured during previous weekends may be used to predictfuture weekend power consumptions, since the building is likely atminimum occupancy during these times.

Process 800 is further shown to include storing agreed-upon ranges ofcontrollable input variables and other agreement terms in memory (step806). These stored and agreed-upon ranges or terms may be used asbaseline model assumptions. In other embodiments the baseline model or aresultant contract outcome may be shifted or changed when agreed-uponterms are not met.

Process 800 is yet further shown to include conducting an energyefficient retrofit of a building environment (step 808). The energyefficient retrofit may include any one or more process or equipmentchanges or upgrades expected to result in reduced energy consumption bya building. For example, an energy efficient air handling unit having aself-optimizing controller may be installed in a building in place of alegacy air handling unit with a conventional controller. Once the energyefficient retrofit is installed, a measured energy consumption for thebuilding is obtained (step 810). The post-retrofit energy consumptionmay be measured by a utility provider (e.g., power company), a system ordevice configured to calculate energy expended by the building HVACsystem, or otherwise.

Process 800 also includes applying actual input variables of thepost-retrofit period to the previously created baseline model to predictenergy usage of the old system during the post-retrofit period (step812). This step results in obtaining a baseline energy consumption(e.g., in kWh) against which actual measured consumption from theretrofit can be compared.

Process 800 is further shown to include subtracting the measuredconsumption from the baseline energy consumption to determine potentialenergy savings (step 814). In an exemplary embodiment, a baseline energyconsumption is compared to a measured consumption in by subtracting themeasured consumption during the post-retrofit period from the baselineenergy consumption calculated in step 812. This subtraction will yieldthe energy savings resulting from the retrofit.

Process 800 is yet further shown to include checking the baseline modelassumptions for changes by comparing the calculated energy savings to athreshold parameter (step 816). For example, an EWMA control chart maybe applied to the calculated energy savings to check the validity of themodel assumptions. Such a chart may utilize control limits (e.g.,threshold parameters) generated using a computerized implementation ofprocess 600 or 620. A BMS implementing process 800 may determine if thesavings are outside of the control limits of the chart. If the savingsare outside of the control limits, the BMS may then generate an alert ormay initiate other corrective measures. For example, the BMS may thendetermine new baseline model assumptions (e.g., by repeating step 806)and repeating steps 808-816 to continuously calculate and verify thepotential energy savings for the building.

Referring now to FIG. 9, a detailed diagram of a building managementsystem portion is shown, according to an exemplary embodiment. The logicblocks shown in FIG. 9 may represent software modules of fault detectionand diagnostics layer 114 shown in FIG. 1. Field controller 904 controlsone or more components of the BMS and receives or calculates performancevalues 906 (e.g., sensor inputs, actuator positions, etc.). Controller904 can store a trend of performance values 906, setpoints and currentstatus in local trend storage 908. Trend storage 908 may be a memorydevice that is a component of, coupled to, or located externally tocontroller 904. In one embodiment, the trend sample intervals used tosample performance values 906 are setup during a system configurationprocess. For example, the sample intervals may be less than one half ofthe time constant of the process controlled by controller 904 to preventaliasing. Other sample intervals may also be used, depending upon thetype of data that is sampled.

The trend data in local trend storage 908 may be communicated overnetwork 912 (e.g., the Internet, a WAN, a LAN, etc.) to an EWMA database924 or to an intermediate server between controller 904 and EWMAdatabase 924. In one embodiment, the trend data from local trend storage908 may be provided to delay filter 914. Delay filter 914 removes datathat is likely to contain excessive field controller dynamics.Typically, the delay period for delay filter 914 is greater than orequal to five times the time constant of the process controlled bycontroller 904, although other delay periods may also be used. In someembodiments, delay filter 914 is triggered by a change in the currentstatus of controller 904 or by changes in one or more setpoint valuesfor controller 904.

A performance index calculator 916 may use the outputs of delay filter914 to calculate a performance index. For example, performance indexcalculator 916 may use the setpoint of controller 904 minus performancevalues 906 to determine a performance index. Once a performance indexhas been calculated by performance index calculator 916, outlier remover918 may be used to remove anomalous values. For example, outlier remover918 may utilize the generalized extreme studentized deviate (GESD)method or an anti-spike filter to remove extreme data values.

EWMA calculator 920 may calculate a moving average of the data fromoutlier remover 918, which is sub-sampled by sampler 923. Sampler 923samples the EWMA data to remove or reduce autocorrelation. For example,sampler 923 may utilize a sample interval greater than or equal to fivetimes the time constant of the process controlled by controller 904 tosample the EWMA data, although other sampler intervals may also be used.

In other embodiments, EWMAs may be calculated directly in a controller,such as field controller 902. Field controller 902 receives performancevalues 905 from the controlled process (e.g., measured temperaturevalues, measured power consumptions, or any other data that can be usedto determine if the BMS is operating normally). The EWMA data is thentrended and stored in trend storage 919. Trend storage 919 may be amemory local to controller 902, a memory in a supervisory controllerhaving control over controller 902, or within any other device withinthe BMS. Typically, the trend sample interval time for trend storage 919is set up during system configuration and ranges from 1-60 minutes,although other interval times may also be used.

The trended EWMA data in trend storage 919 is transmitted over network912 to outlier remover 921, which filters outliers from the data. Forexample, outlier remover 921 may use the GESD method, an anti-spikefilter, or another method capable of removing outliers from the data.Outlier remover 921 provides the resultant data to sampler 922, whichsub-samples the data to remove or reduce autocorrelation. Sampler 922may utilize a sample interval greater than or equal to five times thetime constant of the process controlled by controller 902 to sample theEWMA data, although other sampler intervals may also be used. SampledEWMA data from samplers 922, 923 are then stored in EWMA database 924 asa history of EWMA values. In this way, EWMA database 924 may be used totrain or test EWMA with a statistical process control chart.

Using EWMA database 924, the BMS may determine an analysis period orschedule and determine if training has not been performed or ifretraining has been triggered (step 926). If training or retraining isnecessary, the BMS may then determine if a desirable set of trainingdata is available (step 928). For example, training sets of 150-500 datapoints are typically used. Other amounts of training data may also beused, so long as they provide a sufficient history of behavior of theBMS. If an insufficient amount of data has yet to be collected, the BMSmay continue to collect data until reaching a desired amount.

If EWMA database 924 contains a sufficient amount of training data, theBMS may implement process 930 to define a statistical process controlchart. Process 930 includes checking the autocorrelation and setting astatistical process control chart method (step 932). For example,autocorrelation may be checked by calculating a lag one correlationcoefficient. If the coefficient is low, the data is not autocorrelatedand an EWMA method may be used. If the coefficient is high, the data isconsidered to be autocorrelated and an AR method may be used. Under theAR method, an AR-one model may first be fit to the training data. TheAR-res (residuals) of the AR-one model may then be used in other stepsof process 930.

Process 930 is shown to include checking the data for normality (step934). In general, normal data provides better performance thannon-normal data. However, non-normal data may also be used to detectchanges in the behavior of the BMS. Normality may be tested using aLillifors test or any other normality test capable of distinguishingnormal data sets from non-normal data sets.

Process 930 further includes calculating robust estimates of the targetparameter (μ) and the estimator of scale (σ) (step 936). In oneembodiment, the target parameter is the statistical mean of the historyof the EWMA. For example, the simple mean of the data may be calculatedif the data is determined to be normal in step 934. In anotherembodiment, the median of the data is used. In a preferred embodiment,the estimator of scale calculated in step 936 is a robust estimator ofscale, although other estimators may also be used. For example, robustestimators of scale having Gaussian efficiencies of about 58% or about82% may be used.

Process 930 yet further includes calculating the control chart limits(i.e., the one or more threshold parameters) (step 938). For example, anupper control limit (UCL) may be calculated by multiplying the estimatorof scale by a constant value K and adding the result to the targetparameter. In another example, the product of the constant and theestimator of scale may be subtracted by the target parameter to generatea lower control limit (LCL).

Once target parameters have been established using process 930, the BMScan begin to use the generated statistical process control chart todetect changes in the behavior of the BMS. If new EWMA or AR-res valuesare less than the LCL or greater than the UCL, the new values areconsidered to be outliers (e.g., one or more statistically significantoutliers) (step 940). Optionally, the BMS also determines if anexcessive number of outliers have been detected (step 942). For example,the BMS may disregard one or more outliers detected in step 942 beforetaking further action. The number of outliers necessary before takingfurther action may be set manually by a user or automatically by the BMSitself. For example, the BMS may utilize data concerning the operationalstate of controller 902 to determine a threshold number of outliers.

If the BMS determines in step 942 that an excessive number of outliershave been detected, the BMS may present an indication to a user via adisplay device (step 944). Alternatively, or in addition to step 944,the BMS may take any number of other measures if a change in behaviorhas been detected. For example, the BMS may initiate a diagnosticsroutine, send a communication to a technician (e.g., email, textmessage, pager message, etc.), retrain the statistical model, or anyother appropriate action that corresponds to the change in behavior.

Automated Fault Detection and Diagnostics Using Abnormal EnergyDetection

Text above relating to FIG. 4 describes a process for using abnormalenergy detection to conduct automated fault detection and diagnostics.Such systems and methods can use energy meter data (e.g., electric, gas,steam, etc.) to help detect building system or building device faults.The energy usage measurements used in automated fault detection anddiagnostics can be measurements of either the total building energyusage (e.g., using whole-building meters) or energy usage for a portionof the building management system (e.g., using sub-meters). Variousmethods for detecting abnormal energy usage in buildings are alsodescribed by John E. Seem. See, for example, U.S. Pat. No. 6,816,811(inventor John E. Seem) “Method of Intelligent Data Analysis to DetectAbnormal Use of Utilities in Buildings”; John E. Seem, PatternRecognition Algorithm for Determining Days of the Week with SimilarEnergy Consumption Profiles, Energy and Buildings, vol. 37, 127-139(2005); John E. Seem, Using Intelligent Data Analysis to Detect AbnormalEnergy Consumption in Buildings, Energy and Buildings, vol. 39, 52-58(2007); Seem et al., Adaptive Methods for Real-Time Forecasting ofBuilding Electrical Demand, ASHRAE Transactions, pt. 1., 710-721 (1991).FIGS. 10-14 and 16 describe exemplary processes that may be conducted byan FDD layer (e.g., the FDD layer described with reference to FIG. 4) oran FDD module or modules of another building automation systemcontroller.

Referring now to FIG. 10, a flow diagram of a process 1000 for theidentification of energy outlier days is shown, according to anexemplary embodiment. Process 1000 includes an energy metering step(step 1002) where energy usage is measured by a meter or multiplemeters. The meters measure the consumption and/or demand of an energyutility, such as electricity, gas, steam, hot water, chilled water, etc.The meters may be owned by a building owner, the utility that providesthe energy utility, and/or any other entity or combination of entities.The energy measurements may be for a campus of buildings (e.g., a groupof related building), an individual building, a sub-set or portion of abuilding, or any other aggregation or disaggregation of energy usage.

Process 1000 further includes a trend data capture step (step 1004). Instep 1004, a multitude of measurements collected over time (e.g., fromstep 1002) are stored. The measurements are stored in an electronicstorage device local to the site, remote from the site, or anycombination of devices both local and remote from the site.

Process 1000 further includes a data processing trigger step (step1006). In step 1006, a triggering action that initiates the remainingdata processing steps in process 1000 is activated. The trigger may betime-based (e.g., every day at midnight or another set time, every week,every hour, etc.), user-based (e.g., by receiving an input from a userat a graphical user interface), event-based (e.g., when commanded by autility, when an energy usage threshold is triggered, when the localstorage buffer is full), or may use any other basis to initiateprocessing.

Process 1000 further includes a data cleansing step (step 1008). In step1008, one or more sub-steps are taken to ensure that the data is in anappropriate form for further processing. Data cleansing may include, butis not limited to, checking for data gaps, checking for impropertimestamps, checking for unreasonable values and improperly formattedvalues, and flagging or correcting data with these or any otherproblems. For example, bad data may be replaced via linear interpolationor by replacing the data with a “not a number” (NaN) value.

Process 1000 further includes calculating key values or features fromthe data (step 1010). Step 1010 may include, for example, a dailyfeature extraction (e.g., calculations for daily energy usage). In oneembodiment, step 1010 includes calculating the daily energy consumptionand the daily peak energy demand. In other embodiments, step 1010 caninclude calculating other statistics to describe daily energy use data,calculating to determine moving averages, or other calculations.

Process 1000 further includes identifying and grouping energy featuresby day type (step 1012). Step 1012 may include, for example, using apattern recognition approach to identify and group days of the week withsimilar energy features. Step 1012 may be used to identify patterns inenergy usage for analysis, as day-to-day energy usage patters may differsignificantly. For example, for many commercial buildings, thedifference in energy usage between weekdays and weekends may besignificant. Therefore, all weekdays may be grouped together and allweekend days may be grouped together.

Process 1000 further includes identifying energy outliers (step 1014).Step 1014 may include identifying the outliers in the daily featuresusing the day-type groups identified in step 1012. In one embodiment, aGeneralized Extreme Studentized Distribution (GESD) approach may be usedfor identifying energy outliers, as described in FIG. 9.

FIGS. 11-14 represent unique extensions to process 1000. Referringspecifically to FIG. 11, a flow diagram of a process 1100 for monetizingpotential financial impact of energy outliers and filtering the outliersis shown, according to an exemplary embodiment. Monetization involvesthe calculation of the potential financial impact of each energyoutlier. A filter may be applied to the energy outliers based onmonetization. For example, using a default or user-defined threshold,energy outliers that do not have a significant financial impact may befiltered out before such energy outliers are presented as faults. Such afilter may advantageously minimize the total number of faults that arepresented to a building manager on a graphical user interface (e.g., asshown above). Monetization may also allow a building manager toprioritize faults and corrective actions for the faults based on theestimated financial impact of the faults.

Process 1100 includes determining upper and lower limits for dailyenergy consumption and daily peak energy demand for an average or“normal” day (step 1102). In one embodiment, the limits are calculatedusing the robust mean and robust standard deviation determined inprocess 1000. The limits are calculated as the robust mean plus or minusa set value (e.g., two or three standard deviations). In one embodiment,a normal day is based on the average energy use or demand of a pluralityof recent days of the same day-type (e.g., weekend, weekday) afterremoving any outlier days.

Process 1100 further includes replacing outlier values of the dailyenergy consumption and/or daily peak energy demand with a morereasonable or normal value from step 1102 (step 1104). For example, ifan outlier value is greater than the upper normal limit determined instep 1102, the outlier value is replaced by the upper normal limit, andif the outlier value is less than the lower normal limit determined instep 1102, the outlier value is replaced by the lower normal limit.

Process 1100 further includes storing the daily features calculated inprocess 1200 and the replacement values calculated in step 1104 (step1106). Storage is provided by an electronic storage device local to thesite, remote from the site, or a combination of both.

Process 1100 further includes retrieving the daily data from storage andcreating a set of data for each day to be analyzed (step 1108). In oneembodiment, the set of data for each day to be analyzed includes the dayand the 29 previous days. In other words, a 30 day sliding window ofenergy usage is generated. Two 30 day data sets may be created for eachday, one with outliers and one without outliers included.

Process 1100 further includes defining cost factors to be used tomonetize the outliers (step 1110). The cost factors may be user-definedwhen the software is being configured. Cost factors are required forenergy consumption and/or energy demand Blended or marginal cost factorsmay be provided. Furthermore, in varying embodiments of the disclosure,the values used throughout the processes described herein may relate toor use a blended energy value (blend of consumption/demand) rather thanone or the other.

Process 1100 further includes calculating the average daily cost usingthe data without the outliers (step 1112). The energy cost factors(e.g., consumption and/or demand utility rate information) aremultiplied by the energy consumption and energy demand for each 30 dayset of data. The resulting consumption and demand cost estimations aresummed to obtain the total costs. The total costs are then divided bythe 30 days to obtain the average daily cost. Each 30 day set of data isused to calculate an average daily cost, which is uniquely associated tothe analyzed day (e.g., the last day of the 30 day window). In otherwords, every day is treated as if it were the last day of a billingperiod that is exactly 30 days long; an estimated utility bill iscalculated for each of these billing periods and divided by 30 days todetermine the average daily cost, which is associated with the last dayof the 30 day billing period.

Process 1100 further includes calculating the average daily cost usingthe data with the outliers (step 1114). The calculation of step 1114 maybe the same as the calculation in step 1112, but with the data outliersbeing included in the calculation.

Process 1100 further includes subtracting the average daily costcalculated in step 1112 from the average daily cost calculated in step1114 (step 1116). The result is an estimate of the daily financialimpact of the outlier.

Process 1100 further includes obtaining a user defined-threshold for acost filter (step 1118) to which the estimate of the daily financialimpact of the outlier will be subjected. The threshold may be definedduring configuration or changed at anytime by a user. In otherembodiments, the threshold may be adjusted automatically based onstatistical confidence levels or other calculated values. The thresholdfor the cost filter allows a user to define the financial impact thatthey consider to be significant for an outlier.

Process 1100 further includes filtering the energy outliers determinedin process 1000 using the user defined threshold from step 1118 (step1120). In other words, step 1120 includes determining that an energyoutlier is a fault when it exceeds a certain financial impact threshold.

Process 1100 further includes a presentation of the fault and its costs(step 1122). The presentation may be made through various graphical userinterfaces, user interface “dashboards”, via on-line and off-linereports, via email, text message, or via any other medium. In anexemplary embodiment, the graphical user interface formats shown abovewith respect to chiller energy and cost outliers may be utilized toreport energy and/or cost outliers for other pieces of equipment (e.g.,cooling towers, air handling units, boilers, lighting systems, etc.).

In an exemplary embodiment, the calculations of process 1100 areperformed primarily by an FDD layer such as FDD layer 114 of FIG. 1A.Outputs of the fault determination, associated energy use, associatedenergy costs, and the like may be reported to an enterprise integrationlayer (e.g., layer 108 shown in FIG. 1A) for transforming such outputsinto information (e.g., graphical user interfaces) for providing to alocal electronic display or to a remote source such as a client runningan enterprise control application 124 or a monitoring and reportingapplication 120.

Referring now to FIG. 12, a process 1200 for identifying weatheroutliers and using the weather outliers to further filter energyoutliers is shown, according to an exemplary embodiment. Energy usage inmost buildings is correlated to weather conditions. The presence ofweather outliers may result in energy outliers, which may not need to beidentified as faults if the building is responding as expected to theweather. Process 1200 is shown as an extension of process 1000. Althoughshown separately, in some exemplary embodiments, one or more of theextensions to process 1000 may be applied together (e.g., in series orin parallel).

Process 1200 includes measuring the outside air temperature (step 1202).The measurement may be made by a local air sensor used by the buildingautomation system or on-site weather station. In some embodiments, theoutside air temperature used in process 1200 could be obtained from anexternal source (e.g., a weather station database maintained andoperated by the U.S. National Oceanic and Atmospheric Administration(NOAA)) via communications electronics or storage media of a smartbuilding manager. While temperature is particularly shown in FIG. 12, inother embodiments, other weather parameters may be tracked and utilizedfor energy outlier filtering. For example, humidity, average dailytemperature, peak temperature, a blend of temperature and humidity, orany other weather describing information may be used and processed.

Process 1200 further includes storing a multitude of weathermeasurements over time via a local trend capture (step 1204). In anexemplary embodiment, step 1204 may be similar to step 1004 of process1000.

Process 1200 further includes receiving a triggering action andconducting related triggering activities (step 1206) that initiate theremaining weather data processing steps in process 1200. Step 1206 maybe similar to the triggering step 1006 of process 1000 (e.g., thetriggering may be time-based or event-based).

Process 1200 further includes a data cleansing step to ensure that thedata is in an appropriate form for further processing (step 1208). Step1208 may be similar to the data cleansing step 1008 of process 1000.

Process 1200 further includes calculating key values or features fromthe weather data (step 1210). In one embodiment, the key weatherfeatures calculated may be the daily maximum temperature, the averagetemperature, and the minimum outside air temperature. Process 1200further includes identifying outliers in the daily weather featurescalculated in step 1210 (step 1212). In one embodiment, a GESD approachmay be used to identify weather outliers.

Process 1200 further includes filtering the energy outliers (step 1214)identified in process 1000 using the outliers found in step 1212. Inother words, if an energy outlier occurs on the same day that a weatheroutlier occurs, then the energy outlier may be excluded fromconsideration as a fault. The use of the weather filter reduces thenumber of false or insignificant faults based on the assumption that thebuilding responds to extreme weather conditions as expected and theresulting energy usage increase is unavoidable. Process 1200 furtherincludes presenting the filtered energy outliers as faults (step 1216).

Referring now to FIG. 13, a flow diagram of a process 1300 for applyingthe general outlier analysis approach of process 1000 to real-timemonitoring of energy usage is shown, according to an exemplaryembodiment. Process 1300 is shown as an extension of process 1000.Process 1300 is intended to provide a user or building operator animmediate notice (e.g., nearly immediate, near real-time) when abuilding's energy usage or demand is abnormal.

Process 1300 includes using data from the GESD outlier analysis of step1014 of process 1000 to estimate the threshold on the daily peak energydemand that results in a day being flagged as an outlier (step 1302).

Process 1300 further includes using the threshold from step 1302 and theday-type grouping from step 1012 of process 1000 to reset or adjust thealarm limit on the energy meter point in the building automation system(step 1304). In an exemplary embodiment, the thresholds determined forthe last day processed in step 1302 for each day-type group are used asthe alarm limit for all future days that have the same day-type.Thresholds may be passed from step 1302 to step 1304 on a daily basis.In other embodiments, less frequent threshold estimations may beprovided to step 1304.

Process 1300 further includes monitoring energy demand (step 1306).Process 1300 further includes activating a real-time alarm notificationwhen the energy demand monitored in step 1306 exceeds the alarm limitdetermined in steps 1302-1304 (step 1308). Alarm notification,presentation, acknowledgement, and tracking may all be handled with thestandard protocols implemented in the building automation system.

Referring now to FIG. 14, a process 1400 for pro-active monitoring ofenergy usage is shown, according to an exemplary embodiment. Process1400 is shown as an extension of process 1000 of FIG. 10. Process 1400is used to generate advanced notice of faults by predicting when abuilding's energy usage is likely to become abnormal. Forecasts of thehourly energy usage for the day are used with estimated outlierthresholds to predict abnormal energy usage. The pro-active notificationgives operators more time to take corrective actions to reduce oreliminate additional energy usage and costs.

Process 1400 includes storing trend data and day-type information fromprocess 1000 (step 1402). Storage is provided by an electronic storagedevice local to the site, remote to the site, or a combination of thetwo. Process 1400 further includes forecasting energy usage (step 1404).Step 1404 may be executed, for example, every hour, and may forecast theenergy usage profile for the remainder of the day. In one embodiment,the forecasts are made using an approach defined by Seem and Braun inAdaptive Methods for Real-Time Forecasting of Building Electrical Demand(1991), ASHRAE Transactions, p. 710-721.

Process 1400 further includes using data from the GESD outlier analysisof step 1014 of process 1000 to estimate thresholds for both dailyenergy consumption and daily peak energy demand that results in a daybeing flagged as an outlier (step 1406). Process 1400 further includesusing the forecasted energy profile from step 1404 and the thresholdsfrom step 1406 to predict if and/or when energy usage is likely to beconsidered abnormal (step 1408). Process 1400 further includes an alarmnotification (step 1410). For example, a building operation may benotified when energy usage is predicted to be abnormal (e.g., viadashboards, email, text messages, etc.).

Automated Chiller Fault Detection and Diagnostics Using Model-BasedChiller Performance Analysis

Water cooled chillers represent the single largest energy consumer inmany larger commercial buildings and campuses. The performance of achiller is most commonly checked periodically (e.g., 3-4 times a year)by a mechanic as a part of routine service using manual calculationswith very limited operating data.

Embodiments disclosed herein provide systems and methods forcontinuously and automatically evaluating chiller performance. A chillerperformance model is used to detect potential faults of a chiller (e.g.,a water-cooled centrifugal chiller). Significant deviation of the actualchiller performance from the modeled performance is used as a generalindicator of a fault. A building operator or chiller mechanic may thenperform a follow-up investigation to validate the fault, identify rootcauses for the fault, and to initiate repairs. The model-based approachdescribed herein may be implemented in a chiller controller, in an FDDlayer as described above, or in another system or device that isconfigured to retrieve data from the building automation system orchiller.

In an exemplary embodiment, a modified Gordon-Ng “grey-box” model, withparameters derived from conservation of mass and energy principles, isused to identify faults. The traditional Gordon-Ng chiller performancemodel (e.g., as described in J. M. Gordon and K. C. Ng, CoolThermodynamics (2000)) for mechanical chillers includes severalvariations of the model for use with linear regression. One such form ofthe Gordon-Ng model was proposed for chillers in systems with variablecondenser water flow rates. However, the traditional Gordon-Ng modelforms did not include a version suitable for use with variableevaporator water flow rates. Applicants hereby present a modified formof the Gordon-Ng model for better predicting the performance of chillersoperating with variable evaporator and/or variable condenser flow rates.This modified Gordon-Ng model may also offer improved performance forconstant flow systems where the actual evaporator and/or condenser flowrates vary in practice.

In an exemplary embodiment, the modified form Gordon-Ng model is asfollows: y=a₁x₁+a₂x₂+a₃x₃+a₄y₄

where:

$y = {{\frac{T_{e}}{T_{c}}\lbrack {1 + \frac{1}{C\; O\; P}} \rbrack} - 1}$

and:

$x_{1} = \frac{T_{e}}{Q_{e}}$$x_{2} = \frac{T_{c} - T_{e}}{T_{c}Q_{e}}$$x_{3} = {\frac{Q_{e}}{T_{c}{\overset{.}{m}}_{c}{Cp}_{c}}\lbrack {1 + \frac{1}{C\; O\; P}} \rbrack}$$x_{4} = {\frac{Q_{e}}{T_{c}{\overset{.}{m}}_{e}{Cp}_{e}}\lbrack {1 + \frac{1}{C\; O\; P}} \rbrack}$

and:

a₁ = Δ S_(int) a₂ = Q_(leak)^(eqv) $a_{3} = \frac{1}{ɛ_{c}}$$a_{4} = \frac{1}{ɛ_{e}}$

and:

${C\; O\; P} = \frac{Q_{e}}{{\overset{.}{W}}_{ch}}$

and:

T_(e)=Evaporator water inlet temperature

T_(c)=Condenser water inlet temperature

Q_(e)=Rate of energy transfer across the evaporator heat exchanger

COP=Coefficient of performance

{dot over (W)}_(ch)=Chiller power

{dot over (m)}_(c)=Mass flow rate of condenser water

{dot over (m)}_(e)=Mass flow rate of evaporator water

Cp_(c)=Specific heat of water at the condenser temperature

Cp_(e)=Specific heat of water at the evaporator temperature

ε_(c)=Condenser heat exchanger effectiveness

ε_(e)=Evaporator heat exchanger effectiveness

ΔS_(int)=Total internal entropy production rate in the chiller due tointernal irreversibilities

Q_(leak) ^(eqv)=Rate of heat losses or gains to the chiller

The above chiller model may be populated off-line with data from themanufacturer of the chiller to create a “manufacturer's baseline” model.This data can come from a manufacturer's acceptance tests, previouslaboratory tests, well-controlled field tests, or from manufacturer'ssoftware. The chiller model is used to predict the performance of thechiller operating as designed.

If there is no data available from the manufacturer, then data from theactual chiller can be used to create and maintain a “best baseline”model of performance. While a “best baseline” model may be calculated ina variety of ways, in one embodiment, the “best baseline” model isidentified by repeatedly comparing current chiller performance againsthistorical performance values. Model coefficients are calculated usingchiller performance parameters associated with the ‘best baseline’instance of the chiller.

As illustrated in greater detail in the diagram of FIG. 15, a system fordetecting faults in a chiller may make a fault determination when thechiller's actual performance deviates significantly from the performancepredicted using the “manufacturer's baseline” or “best baseline” models.FIG. 15 shows an exemplary system 1500 for monitoring the performance ofa water-cooled chiller. System 1500 includes sensors 1501 configured toprovide a variety of measurements. The measurements provided by sensors1501 may include:

T_(e,w,i)—temperature of the water entering the evaporator of thechiller

T_(e,w,o)—temperature of the water leaving the evaporator of the chiller

dP_(e)—the water pressure drop across the evaporator

T_(c,w,i)—temperature of the water entering the condenser of the chiller

T_(c,w,o)—temperature of the water leaving the condenser of the chiller

dP_(c)—the water pressure drop across the condenser

{dot over (W)}_(ch)—total electric power used by the chiller

The above measurements may be provided to the above-presented modifiedGordon-Ng model for calculating one or more performance parameters ofthe chiller. As can be seen above, some chiller models (e.g., such asthe above-presented modified Gordon-Ng model) calculate chillerperformance using a linear regression model having coefficientscalculated based on the variables of evaporator and condenser water flowrates. In exemplary embodiments of the present disclosure, pressuredifferential sensors are installed within the chiller to measure thewater pressure drops across the evaporator dP_(e) and across thecondenser dP_(c). The sensor readings dP_(e), dP_(c) are provided to acomputer system (e.g., an FDD-layer of a chiller controller or a smartbuilding manager serving the chiller controller). The water pressurereadings dP_(e), dP_(c) are then used in estimation of the water flowrates. For example, the mass flow rate of the condenser water ({dot over(m)}_(c) of the above model) may be estimated using a proportionalrelationship between the condenser pressure drop and the condenser flowrate raised to a power determined by testing (e.g., manufacturer tests,on-side tests, exponents estimated to be a reasonable default, etc.).Likewise, the mass flow rate of the evaporator water ({dot over (m)}_(e)of the above model) may be estimated using a proportional relationshipbetween the evaporator pressure drop and the evaporator flow rate raisedto a power determined by testing.

In other embodiments, the evaporator and condenser flow rates {dot over(m)}_(e), {dot over (m)}_(c) are measured directly with flow meters.

The sensors 1501 used for the measurements can be factory mounted, fieldinstalled, or some combination thereof. The sensors can provide data tothe chiller's control panel directly, to a building automation system(e.g., smart building manager), or to any electronic hardware or systemsfor measuring the points.

The measurements provided from sensors 1501 are shown as stored in localtrend data 1502 (e.g., within a data server local to the chiller, withina chiller controller's storage). The data can be stored in memory or adatabase that is part of the building automation system or within anyother system configured to store sensor data. The trend samplinginterval times may be set during the system configuration. The trendsampling times, for example, may range from one to fifteen minutes.Shorter or longer sampling times may be set depending on the dynamics ofthe system. The trend sampling times may be changed post systemconfiguration.

A network 1503 (e.g., Internet, WAN) may communicably couple the localtrend data 1502 and central storage 1504. Central storage 1504 and thedownstream modules shown in FIG. 15 may form a part of an FDD layer asdescribed in previous Figures. In other embodiments, central storage1504, data processing trigger 1505, and/or data cleanser 1506 areexternal of the FDD layer and are otherwise located. Data processingtrigger 1505 initiates the downstream components (e.g., data cleanser1506, operational filters 1507, etc.) to proceed with processing data inthe central storage 1504. Data processing trigger 1505 can be time-based(e.g., run every day, run every week, etc.) be condition-based (e.g.,run based on an out-of-bounds temperature, run based on an out-of-boundsenergy use detection), or can be user triggered (e.g., require that theuser press a button to initiate further processing). Data processingtrigger 1505 can also receive and respond to ‘push’ analysis commandsfrom the chiller itself in response to simple thresholds or otheractivities of the chiller controller.

Data cleanser 1506 is configured to conduct processing to place data ina better form for further processing. Such processing may include, forexample, checking for data gaps, checking for improper timestamps,checking for unreasonable values, and flagging for correcting data withthese or any other data problems.

Operational filters 1507 can use the information output by data cleanser1506 (e.g., use the flagging created by data cleanser 1506) to remove orreplace sets of data. Operational filters 1507 can also use feedbackfrom the chiller itself (e.g., data regarding when the chiller was shutdown, operating with power below normal operating levels, operating withan evaporator cooling load below minimum load conditions, etc.) toremove sets of data. Operational filters 1507 may include additionalfilters for removing sets of data that include bad readings or thatrepresent unrealistic or questionable operating conditions.

Energy balance filter 1508 removes sets of data that do not reasonablysatisfy an overall energy balance of the chiller. Accordingly, theoverall energy balance of the chiller is used to filter the data outputfrom filters 1507. The energy balance may be used as an indicator ofdata quality, an indicator of sensor faults, or a pseudo steady-statedetector. Steady-state detection may be important as many chiller modelswill not be accurate in less than a steady-state (or near steady-state).Therefore, energy balance filter 1508 helps remove data sets with baddata or data sets associated with the chiller not operating insteady-state (or near steady-state) conditions. In alternativeembodiments, approaches other than evaluating energy balance may be usedfor steady-state detection.

Modeled performance calculation module 1509 calculates performance usingone or more of the stored models (e.g., a Gordon-Ng linear regressionmodel), and the current data cleaned by data cleanser 1506, operationalfilters 1507, and/or energy balance filter 1508.

If a manufacturer's baseline model is available, then modeledperformance calculation model 1509 uses the manufacturer's baselinemodel to predict the performance of the chiller operating under anassumption that it was new and under the current operating conditions(e.g. T_(c,w,i), T_(e,w,o), Q_(e)). As explained above, a “basebaseline” model may also or alternatively be used to predict theperformance of the chiller operating under the current operatingconditions but at peak historical performance.

Model-based performance index calculation module 1510 calculates aperformance index for the chiller. In an exemplary embodiment, theperformance index may be calculated by finding the difference betweenthe actual chiller performance and the model-predicted performance. Inan exemplary embodiment, power is used to gauge chiller performance andthe performance index is calculated as the actual chiller power minusthe model-predicted power.

The statistical process control (SPC) chart fault detector 1511 uses ahistory of the performance index and the newly calculated performanceindex to identify chiller faults. Faults are detected by detector 1511when the chiller's actual performance deviates significantly from thepredicted performance (e.g., predicted using the “manufacturer baseline”or “best baseline”). The SPC chart fault detector 1511 uses astatistical process control chart method to identify chiller faults. Inan exemplary embodiment, an EWMA control chart method is conducted on anEWMA of the error between the actual and predicted performance. In otherwords, detector 1511 compares the exponentially weighted moving averageof the error between actual and expected chiller performance to athreshold (e.g., calculated as described above). The detector 1511determines whether to identify the error as associated with a chillerfault based on whether the exponentially weighted moving average exceedsthe threshold. In other embodiments, statistical process control chartmethods other than an EWMA method may be used to identify performanceoutliers inferred to represent chiller faults.

Bin data module 1512 reduces the input data using a binning approach toprovide more uniform sets of data for comparison. The data binningapproach is intended to improve the predictive capabilities of chillermodels created using chiller data. Particularly, the approach isintended to reduce the effect of non-linearities in chiller performancethat could lead to model biases. In an exemplary embodiment, bin datamodule 1512 completes the following steps for any given set of chillerdata:

(a) finds a minimum and maximum value for each key sensor point used bythe model;

(b) divides the range of values between the minimum and maximum valueinto a number of discrete segments (i.e., bins);

(c) sorts every data set from the analysis period into a single bin ofthe created plurality of discrete bins;

(d) for each bin, finds a single point (e.g., median point, mean value,etc.) to represent all of the points in the bin;

(e) the selected single point is used as the chiller data for the periodof time associated with the bin.

Using such a process, bin data module 1512 can have the effect of makingthe chiller data selected for further analysis become more uniform andrepresentative of the chiller data without non-linearitities.

The module for fitting actual performance data 1513 fits a model to thechiller data output by bin data module 1512. In an exemplary embodiment,the Gordon-Ng model modified for variable evaporator and condenser flowrates (presented above) is fit to the data using linear regression. Inan alternative embodiment, a step-wise regression is used to find thebest form of the model. In yet other embodiments, non-linear regressionis used to fit more complex models. The model fit to the actual data maybe called the “actual” model. The “actual” model may be used tocalculate various normalized metrics and compared to other models ormetrics.

IPLV calculation module 1514 calculates the integrated part-load value(IPLV) as defined by ASHRAE Standard 550/590 using each of the chillermodels. The IPLV is a normalized metric that is used in the chillerindustry for making comparisons between chillers. The IPLV provides asingle value descriptive of performance versus a curve or family ofcurves. Because of the single value property of the IPLV, the IPLV canbe used for a quick comparison of chiller performance. For example, theIPLV may be used to compare the actual chiller's performance against newchillers to justify a chiller retrofit. Over time a chiller's overallcondition varies significantly as faults arise and faults are corrected.The IPLV computed from the actual model may be used to identify the“best baseline” performance achieved by the chiller. The “best baseline”model is stored and used for future fault detection and performancereporting. The “best baseline” model may be used as an alternative tothe “manufacturer's baseline” model. For older chillers, data may not beavailable for creating a “manufacturer's baseline” model. Although the“best baseline” model may initially be based on a chiller with faults,as faults are corrected over time, the “best baseline” model may providea reasonable performance target. In an exemplary embodiment, IPLVcalculation module 1514 calculates an IPLV for the actual performancemodel and stores an IPLV previously calculated for a “best” performancemodel currently in use by modeled performance calculation module 1509.

Best performance determiner 1515 uses the IPLV(s) determined by IPLVcalculation module 1514 to check the performance of the current chilleragainst the performance of the chiller during all previous analysisperiods (or relative to the previously determined ‘best’ as in-use bymodeled performance calculation module 1509). If the current performanceis deemed to be the best, the current ‘best baseline’ model is replacedby the actual model, using model updater 1516. The new ‘best baseline’model may thereafter be used by modeled performance calculation model1509. In an exemplary embodiment, best performance determiner 1515selects the model having the highest IPLV of the coefficient ofperformance (COP) as having the best performance. In another embodiment,best performance determiner 1515 integrates the COP vs. evaporator loadcurve to find a performance metric for comparison. In anotherembodiment, a load weighted-average COP is calculated using actualoperating conditions.

The module for monetizing performance and faults 1517 is configured tomonetize the actual chiller's performance and potential faults. Anelectric rate schedule is applied to the actual chiller power data tomonetize actual performance of the chiller. The electric rate is alsoapplied to the predicted power using the “manufacturer's baseline” or“best baseline.” If a fault is identified by SPC chart fault detector1511, then the difference between costs using the actual power and thecosts using the predicted power provides an estimate of the fault costs.

In an exemplary embodiment, chiller faults are monetized using blendedconsumption ($/kWh) and demand (e.g., $kW) cost factors. These factorsmay be computed from the most recent year of utility bills. These costfactors may be applied to the energy consumption and demand calculatedover a defined period (e.g., a calendar month) using the actualperformance data and the predicted performance using the manufacturer'sbaseline or best baseline models.

Performance and fault presentation module 1518 may present theinformation calculated or obtained by the various prior modules of FIG.15. The presentation module 1518 may cause graphs, summaries, reports,graphical user interfaces, interactive interfaces, or otherpresentations of information to be displayed to building operators orowners (e.g., via one or more client devices). Such presentations mayinclude dashboard views, e-mail summaries, text message alerts, or otherreports.

Fault Management Systems and Methods

Referring now to FIG. 16, a block diagram of a fault detection module1602 and fault analysis module 1604 is shown, according to an exemplaryembodiment. Fault detection module 1602 receives data from building data1606 and conducts fault detection methods as generally described in thepresent disclosure. In an exemplary embodiment, the components of FIG.16 may be implemented within an FDD layer (e.g., an FDD layer asdescribed with reference to FIG. 1) of a smart building manager.

Fault detection module 1602 provides fault information to fault analysismodule 1604. Fault information may include statistical outliersindicating the presence of a fault, a magnitude of the fault (e.g., howsevere the fault is) and other fault properties. Fault analysis moduleincludes a diagnostics module 1610 configured to receive faultinformation. Diagnostics module 1610 is configured to identify a rootcause for the fault based on fault information. Diagnostics module 1610may further determine critical or enhanced data or information relatedto the fault.

Diagnostics module 1610 provides the root cause information to a faultmonetization module 1612. Fault monetization module 1612 is configuredto determine a cost associated with the fault (e.g., how much the faultis costing the owner of the building). The cost may be in terms ofdollars, energy use, or any other type of metric. Fault monetizationmodule 1612 may provide the cost information to visualization module1608 for display to a user.

Fault cost information from fault monetization module 1612 is providedto a fault prioritization module 1614. Fault prioritization module 1614is configured to use the fault cost information to prioritize betweentwo or more faults. For example, the fault with the higher cost may begiven a higher priority (e.g., the fault may be listed first in a reportor display, the fault may be presented to the user before the otherfault, etc.). After determining fault priority, scheduling module 1616is configured to generate a schedule for addressing the prioritizedfaults. Scheduling module 1616 may, for example, create a maintenanceschedule for inspecting and fixing faults in the building. The schedulemay be based on faults that have a higher priority or cost.

The schedule for resolving faults is provided to a fault resolution andtracking module 1618. Fault resolution and tracking module 1618 isconfigured to verify if a fault was fixed and if the fix was effective.Fault resolution and tracking module 1618 may conduct the check when theschedule indicates that the fault resolution should have occurred. Faultresolution and tracking module 1618 may further include and maintaininformation about a fault life timeline. For example, if a faultresolution is only expected to last for a period of time, or if amaintenance schedule should be associated with a fault resolution,module 1618 may keep track of such information and conduct checks whennecessary. Fault resolution and tracking module 1618 may furthergenerate reports related to the tracking of fault resolutions. Thereports may include cost information (e.g., money saved by conductingthe fault resolutions). These reports may be provided to user devices1624 via an enterprise application interface 1622.

Visualization module 1608 is configured to receive fault information(e.g., the magnitude of the fault, a cost associated with the fault)from various modules and may generate a user interface for providing toan electronic display or remote device. The graphical user interface mayinclude the number of faults, the number of faults relative to the totaldata (e.g., a percentage or ratio of faults compared to non-faultyequipment), and other fault information. For example, the severity of afault may be detailed, the cost of the fault may be detailed, etc. Thefaults may be grouped by type of fault, equipment involved in the fault,cost or severity of the fault, etc. As shown in FIG. 16, the graphicaluser interface may be provided from visualization module 1608 to userdevices 1624 via user input/output module 1620. Illustrations ofpossible exemplary user interfaces are contained in FIGS. 17A-17 e.

Graphical User Interfaces for Automated Fault Detection and Diagnostics

Referring to FIGS. 17A-E generally, various exemplary graphical userinterfaces that may be generated via step visualization or userinput/output steps of the various processes described herein are shown.For example, the graphical user interfaces of FIGS. 17A-E may relate tooutput from block 1608 of FIG. 16, step 1018 of process 1000, block 944of FIG. 9, step 1122 of FIG. 11, step 1216 of FIG. 12, step 1308 of FIG.13, step 1410 of FIG. 14, block 1518 of FIG. 15, or other user interfaceor presentation blocks/steps of the present application. It should beappreciated that the exemplary user interfaces of FIGS. 17A-E are notintended to limit the output that can be provided by theprocesses/blocks of FIGS. 1A-16, but rather are intended to describevarying graphical user interface embodiments or inventions that may beclaimed in this or another matter.

Referring now to FIG. 17A, a report such as table 1700 may be generatedfor display to a user of a building, according to an exemplaryembodiment. Table 1700 may be a report generated upon user input orrequest or may be automatically transmitted to a remote user device whengenerated when faults are detected. Table 1700 may display faults for abuilding, faults related to a specific area, faults related to aparticular piece of equipment (e.g., chiller) or building subsystem,faults related to a particular tenant, fault type, or otherwise.

In the embodiment of FIG. 17A, table 1700, and particularly form 1702,identifies that faults for a specific customer's process at a site. Fora customer “Company 001” and site “Site 001”, all faults related to aprocess “Chiller 4—York YK” are shown in table 1700. Table 1700 isfurther shown to display usage cost, demand cost, and fault costcalculated to be associated with the chiller (e.g., using the processesdescribed above).

Table 1700 is shown to include rows of fault information (e.g., asdetected using the processes described above). Table 1700 includescolumn 1704 for the date of the fault, a column 1706 for the fault type,and a column 1708 for the equipment associated by the fault (e.g.,affected by the fault or causing the fault). The user may click acheckbox 1710 to hide a fault or to select a fault for analysis.

Referring now to FIG. 17B, another embodiment of a graphical userinterface that may be provided to a local display or remote user deviceis shown, according to an exemplary embodiment. The graphical userinterface of FIG. 17B includes two graphs 1720, 1721 that illustratingoutside air temperature and raw meter data for a collection of days.Graph 1720 shows outside air temperature information over a period ofdays and graph 1721 shows energy usage information over the period ofdays. By viewing the display of FIG. 17B, a user may visualize detectedfaults relative to the outside air temperature and the energy usage data(e.g., to obtain context for the estimated faults). The graphical userinterface of FIG. 17B additionally includes a table 1722 including faultdetails.

Table 1722 includes a column 1723 for the date of the fault. Table 1722also includes a column 1724 showing a normal cost associated with energyusage for a given day. The normal cost may be determined or calculatedbased on past history of energy usage for a given day or time period,based on expected cost given the weather conditions, or on acombinations of factors. Table 1722 also includes an added cost column1725 showing the added cost (actual minus expected). For example, forthe first fault listed, the cost of energy usage was $122 above normalcost. If the added cost is inferred to be attributed to a fault, column1725 allows the user to view the cost associated with a fault. Table1722 further includes a weather column 1726 which may provide the userwith weather condition information which may have affected the energyusage. The information in columns 1724-1726 may be generated using thesame data behind graphs 1720, 1721. Automated FDD methods for evaluatingthe effects of weather on energy usage is described in greater detail inFIG. 12.

Referring further to FIG. 17B, indicia such as the circles at the peaksof the meter data for January 5 and January 6 can graphically identifydetected faults. The system may be configured to allow the user toselect or click on the indicia to obtain more information about thefault (e.g., the cost attributed to the fault). For example, in responseto a user click on the circles identifying fault days January 5 andJanuary 6, more information regarding the faults and their magnitude(e.g., with an indication of added cost, with an indication of distancefrom an expected value or line, with data influencing the faultdetermination, etc.) may be popped up or otherwise shown on a graphicaluser interface (e.g., table 1722 may pop-up in response to a click onone of the circled faults). Advantageously, by providing fault indicia,temperature data, and meter data on the same graphical user interface, auser may be able to determine whether the faults are true or falsepositives. In the example of FIG. 17B, a user might be able to see thatwhile weather was described as “typical” the peaks associated with theJanuary 5 and January 6 “faults” could be attributed (at least in part)to higher than average temperatures. Furthermore, it should beappreciated that FIG. 17B provides an example of a graphicalrepresentation of fault day identifications relative to non-fault dayidentifications.

In the tables of FIGS. 17A-B, the columns of the graphical userinterfaces may be sortable by a user. For example, a user may sort thefaults based on the cost associated with the fault, a date or timestampof the fault, the type of fault, or another property.

Referring now to FIG. 17C, an exemplary graphical user interfacedisplaying chiller performance is shown. Graph 1730 includes showing anupper limit 1732 and lower limit 1734 of manufacturer-estimated chillerperformance described in terms of kW/chiller ton relative to percentageof full load. Graph 1730 further shows actual chiller performance 1736relative to percentage of full load. Since actual energy usage 1736falls outside of the bounds formed by the upper limit 1732 and lowerlimit 1734, an FDD system for a user may determine that there is afault. An FDD system may determine the cost of the fault and report it(e.g., using a table as shown in FIGS. 17A/17B).

Graph 1730 further includes a bar graph 1738 descriptive of actualchiller use. Bar graph 1738 shows for how long (in terms of hours) achiller was operating at a given percent of full load. For example, inthe embodiment of FIG. 17C, the chiller operates for approximately 175hours at 55% capacity, approximately 75 hours at 60% capacity, and soforth. Bar graph 1738 may be used in conjunction with the actual energyusage information displayed in graph 1730 to determine context fordisparities between actual to manufacturer-predicted performance. Forexample, if the chiller had operated at or near 100% capacity at anytime and graph 1730 indicates a fault based on limits 1732, 1734 andenergy usage 1736, the user might be able to predict that chillerperformance was impacted by less than optimal use of the chiller, not bya problem with the chiller. As another example, if the chiller hadoperated at a normal capacity (e.g., as shown in FIG. 17C, the chillermostly operated at around 50-60% capacity) and graph 1730 indicates aperformance disparity, then the user may determine that a fault existsthat can be optimized. By viewing FIG. 17C a user may advantageously beable to view the magnitude of the faulty performance or actualperformance relative to expected values (e.g., manufacturer providedthresholds).

Referring now to FIG. 17D, an exemplary graphical user interface showingraw data used for use in fault detection is shown, according to anexemplary embodiment. In the embodiment of FIG. 17D, graph 1750illustrates chiller energy usage. Graph 1750 includes a plot 1752 of thetemperature of the water leaving the evaporator of the chiller (e.g.,T_(e,w,o)) and a plot 1754 of the temperature of the water entering thecondenser of the chiller (e.g., T_(c,w,i)). Plot 1756 illustrates theenergy usage of the chiller during times associated with plots 1752,1754. According to various embodiments, other plots relating to buildingproperties may be presented (e.g., a plot of outside air temperaturethat may be used to view how outside temperature is affecting chillerenergy usage). FIG. 17D may be displayed by a presentation module inresponse to a user clicking on an identified fault in a first graphicalview (e.g., after clicking on an aspect of FIG. 17C, etc.). Therefore,an aspect of the presentation modules described herein may be to providea user with the ability to “drill down” into the data behind a faultwith mere clicks on user interfaces that first identify the fault to theuser.

Referring now to FIG. 17E, another exemplary graphical user interfacefor reporting building equipment energy outliers is shown, according toan exemplary embodiment. The graphical user interface includes a table1760 that is configured to show statistical outliers. Table 1760includes data values for describing the data and/or statisticaloutliers. In the illustrated example, table 1760 includes four values:FLWERABS (an absolute value of a flow error), FLWERROR (an actual flowerror), TEMPABS (an absolute value of a temperature error), and TEMPERR(an actual temperature error). Table 1760 includes further informationrelating to the statistical outliers such as the campus, building,floor, or other location where the statistical outliers were observed, asystem and name of the system or other equipment associated with thestatistical outliers, and a date and time stamp.

The graphical user interface of FIG. 17E further includes various plotsfor the error values. Plot 1762 plots the FLWERABS values againstfrequency, plot 1764 plots the TEMPABS values against frequency, plot1766 plots the FLWERROR values against frequency, and plot 1768 plotsthe TEMPERR values against frequency. For values for which determined tobe associated with a fault, the plot may be highlighted. For example,bars 1770 are shown highlighted and show the user that the highlightedvalues are associated with a fault. The user may click on shaded bars1770 to receive further information about the faults (e.g., informationsuch as shown in FIG. 17A or 17B). The graphical user interfaces of FIG.17E advantageously allows for faults to be presented relative tonon-fault equipment or data. The graphic 1768, for example, shows thedistribution of faulty equipment relative to non-fault equipment. Inalternative embodiments, the graphic of 1768 may be modified toillustrate faulty data relative to non-faulty data for a single piece ofequipment.

Configurations of Various Exemplary Embodiments

The construction and arrangement of the systems and methods as shown inthe various exemplary embodiments are illustrative only. Although only afew embodiments have been described in detail in this disclosure, manymodifications are possible (e.g., variations in sizes, dimensions,structures, shapes and proportions of the various elements, values ofparameters, mounting arrangements, use of materials, orientations,etc.). For example, the position of elements may be reversed orotherwise varied and the nature or number of discrete elements orpositions may be altered or varied. Accordingly, all such modificationsare intended to be included within the scope of the present disclosure.The order or sequence of any process or method steps may be varied orre-sequenced according to alternative embodiments. Other substitutions,modifications, changes, and omissions may be made in the design,operating conditions and arrangement of the exemplary embodimentswithout departing from the scope of the present disclosure.

The present disclosure contemplates methods, systems and programproducts on memory or other machine-readable media for accomplishingvarious operations. The embodiments of the present disclosure may beimplemented using existing computer processors, or by a special purposecomputer processor for an appropriate system, incorporated for this oranother purpose, or by a hardwired system. Embodiments within the scopeof the present disclosure include program products or memory comprisingmachine-readable media for carrying or having machine-executableinstructions or data structures stored thereon. Such machine-readablemedia can be any available media that can be accessed by a generalpurpose or special purpose computer or other machine with a processor.By way of example, such machine-readable media can comprise RAM, ROM,EPROM, EEPROM, CD-ROM or other optical disk storage, magnetic diskstorage or other magnetic storage devices, or any other medium which canbe used to carry or store desired program code in the form ofmachine-executable instructions or data structures and which can beaccessed by a general purpose or special purpose computer or othermachine with a processor. Combinations of the above are also includedwithin the scope of machine-readable media. Machine-executableinstructions include, for example, instructions and data which cause ageneral purpose computer, special purpose computer, or special purposeprocessing machines to perform a certain function or group of functions.

Although the figures may show a specific order of method steps, theorder of the steps may differ from what is depicted. Also two or moresteps may be performed concurrently or with partial concurrence. Suchvariation will depend on the software and hardware systems chosen and ondesigner choice. All such variations are within the scope of thedisclosure. Likewise, software implementations could be accomplishedwith standard programming techniques with rule based logic and otherlogic to accomplish the various connection steps, processing steps,comparison steps and decision steps.

1. A controller for detecting energy usage faults in a buildingmanagement system, the controller comprising: a processing circuitconfigured to maintain a history of energy usage values for the buildingmanagement system; wherein the processing circuit is configured tocalculate a threshold parameter using a first subset of the history ofenergy usage values defined by a sliding time window, the thresholdparameter establishing an energy usage value limit relative to thehistory of energy usage values; wherein the processing circuit isconfigured to identify energy outlier days by comparing energy use for afirst day with the threshold parameter.
 2. The controller of claim 1,wherein the processing circuit is configured to dynamically update thethreshold parameter by sliding the time window forward in time to definea second subset of the history of energy usage values and using thesecond subset of the history of energy usage values to calculate anupdated threshold parameter; wherein the processing circuit isconfigured to identify energy outlier days by comparing energy use for asecond day with the updated threshold parameter.
 3. The controller ofclaim 2, wherein the sliding time window defines a time period of apredetermined duration ending at a current date; wherein the processingcircuit is configured to slide the time window forward in time and tocalculate the updated threshold parameter in response to updating thecurrent date.
 4. The controller of claim 1, wherein the processingcircuit is configured to calculate a first central tendency of a dailycost using the history of energy usage values with the outlier days anda second central tendency of a daily cost using the history of energyusage values excluding the outlier days; wherein the processing circuitis configured to estimate an additional cost of the energy outlier daysby calculating a difference between the first central tendency and thesecond central tendency; and wherein the processing circuit isconfigured to assign the estimated additional cost of the energy outlierdays to a cost of the energy usage faults in the building managementsystem.
 5. The controller of claim 4, wherein the processing circuit isconfigured to output the cost of the energy usage faults to at least oneof a remote device and a local electronic display.
 6. The controller ofclaim 1, wherein energy usage comprises at least one of demand andconsumption of an energy utility; wherein the energy utility comprisesat least one of an electric utility, a gas utility, a steam utility, anda water utility.
 7. A controller for detecting energy usage faults in abuilding management system, the controller comprising: a processingcircuit configured to maintain a history of energy usage values for thebuilding management system; wherein the processing circuit is configuredto use the history of energy usage values to identify a daily energyusage feature for each of a plurality of days represented in the historyof energy usage values; wherein the processing circuit is configured togroup like days based on the identified daily energy usage features tocreate a plurality of day-type groups; wherein the processing circuit isconfigured to calculate a threshold parameter of daily energy usage foreach day-type group using the history of energy usage values for thedays in the day-type group for which the threshold parameter iscalculated; wherein the processing circuit is configured to identifyenergy outlier days by comparing energy use for a day with one of thecalculated threshold parameters.
 8. The controller of claim 7, whereincomparing energy usage for a day with the threshold parameter comprises:metering energy usage during the day; identifying a day-type groupassociated with the day; and comparing the metered energy usage with thethreshold parameter calculated for the identified day-type group todetermine whether the day is an energy outlier relative to other days inthe same day-type group.
 9. The controller of claim 7, wherein energyusage comprises at least one of demand and consumption of an energyutility; wherein the energy utility comprises at least one of anelectric utility, a gas utility, a steam utility, and a water utility.10. The controller of claim 7, wherein calculating the thresholdparameter for each day-type group comprises: determining a peak energydemand for the days in the day type group for which the thresholdparameter is calculated; and using the peak energy demand as thethreshold parameter for the day-type group.
 11. The controller of claim7, wherein the processing circuit is configured to generate an alarm andto cause the alarm to be transmitted to a remote device in response to adetermination, by the processing circuit, that the day for which energyusage is metered is an energy outlier relative to other days in the sameday-type group.
 12. The controller of claim 11, wherein the processingcircuit is configured to generate the alarm and to cause the alarmtransmission during the day of the metered energy usage.
 13. Thecontroller of claim 11, wherein the processing circuit is configured togenerate the alarm and to cause the alarm transmission in near real-timein response to the metered energy usage exceeding the threshold value.14. The controller of claim 7, wherein the processing circuit isconfigured to identify weather outlier days by comparing a weatherparameter for the day to a history of weather parameters.
 15. Thecontroller of claim 14, wherein the processing circuit is configured todetermine whether the energy outlier day is also a weather outlier day;wherein the processing circuit is configured to exclude the energyoutlier day from consideration as a fault in response to a determinationthat the energy outlier day is also a weather outlier day.
 16. Thecontroller of claim 7, wherein the processing circuit is configured toforecast energy usage during a day and to use the forecasted energyusage to determine whether the forecasted energy usage exceeds thethreshold parameter calculated for the day-type group associated withthe day.
 17. A controller for displaying identified faults in a buildingmanagement system, the controller comprising: a processing circuitconfigured to cause a graphical user interface to be displayed on anelectronic display device, wherein the graphical user interfacecomprises indicia that a fault has occurred; wherein the graphical userinterface is configured to at least one of: (a) graphically representfault days relative to non-fault days, (b) allow a user to click on agraphical indicator for the fault to cause additional information forthe fault to be displayed, (c) allow a user to click on a graphicalindicator for the fault to cause raw data associated with the fault tobe displayed, (d) graphically represent a distribution of fault datarelative to non-fault data, (e) graphically represent a distribution offaulty equipment relative to non-faulty equipment, (f) view a magnitudeof the fault relative to other data or equipment, (g) view an additionalcost associated with an existence of the fault relative to a non-faultcondition, and (h) view an expected energy or monetary cost relative toan actual energy cost or monetary cost.
 18. A computerized method fordetecting faults in building equipment, the method comprising: comparingan actual performance of building equipment to an expected performanceof the building equipment, wherein the comparison comprises calculatingan error between the actual performance and the expected performance;applying results of a statistical analysis to the comparison to identifya deviation from the expected performance, wherein identifying adeviation from the expected performance comprises maintaining anexponentially weighting moving average of the error and comparing theexponentially weighted moving average to a threshold; determiningwhether to identify the error as associated with an equipment faultbased on whether the exponentially weighted moving average exceeds thethreshold; and generating an output signal indicative of an equipmentfault in response to the exponentially weighted moving average exceedingthe threshold.
 19. The computerized method of claim 18, furthercomprising: applying utility rate information to the actual performanceof the building equipment and the expected performance of the buildingequipment to obtain a cost difference; outputting an indication the costdifference as attributed to the equipment fault in response to adetermination that the error should be identified as associated with theequipment fault.
 20. The computerized method of claim 19, furthercomprising: determining whether the actual performance of the buildingequipment for a period of time is better than a previously determinedbest performance storing the actual performance as a new bestperformance parameter in response to a determination that the actualperformance of the building equipment for the period of time is betterthan a previously determined best performance; using the new bestperformance parameter to build a new regression model for calculatingexpected performance in a subsequent period of time.